SITE ID

Codes
- 10 Greater CA
- 20 Georgia
- 25 North Carolina
- 30 Northern CA
- 40 Louisiana
- 50 New Jersey
- 60 Detroit
- 61 Michigan
- 70 Texas
- 80 Los Angeles County
- 81 USC-Other
- 82 USC-MEC
- 90 New York
- 94 Florida
- 95 WebRecruit-Limbo
- 99 WebRecruit

  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  # NEED REGISTRY NAMES!!
 
  
  #replace number with names
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  
  
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  
  #cro(new.d$siteid) # this is pretty but doesn't show NAs
  #summary(new.d$siteid)
  
  #Using kable function to form a nice table
  
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")

Overview of all Registries
Registry	Total
Greater CA.10	315
Georgia.20	1754
Northern CA.30	210
Louisiana.40	585
Detroit.60	356
Michigan.61	16
Los Angeles County.80	321

SURVEY ID

Scantron assigned SurveyID

  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))

## [1] "Number of duplicates: 6"

  print("The following are duplicated IDs:")

## [1] "The following are duplicated IDs:"

  print(dups)

## [1] 101079  100849  300631  200312  201605  211392 
## 3551 Levels: 100037  100050  100059  100061  100064  100072  100073  ... 991774

  print("Number of NAs:")

## [1] "Number of NAs:"

  print(sum(is.na(new.d$surveyid)))

## [1] 0

LOCATION NAME

Name of Registry delivery location

  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  
  #To get the number of each registry
  locationname_count<-count(new.d$locationname)
  colnames(locationname_count)<- c("Location", "Total")
  #To get freq of each registry
  locationname_freq1<-table(new.d$locationname)
  locationname_freq<-as.data.frame(round(prop.table(locationname_freq1),3))
  colnames(locationname_freq)<- c("Location", "Freq")
  #Merge them by "Location"
  result<-merge(locationname_count,locationname_freq,by="Location", sort=F)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")

Overview of Registry delivery location
Location	Total	Freq
Detroit	292	0.082
Georgia	1839	0.517
Greater Bay	210	0.059
Greater California	315	0.089
Los Angeles	321	0.090
Louisiana	500	0.141
Virtual	80	0.022

RESPOND ID

From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.

  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))

## [1] "Number of duplicates: 14"

  print("The following are duplicated IDs:")

## [1] "The following are duplicated IDs:"

  print(dups)

##  [1] 61100276 10100813 10100628 30100177 30100278 30100172 20100653 40102145
##  [9] 10101130 61100327 40100589 20100647 20100647 20103912
## 3543 Levels: 10100003 10100012 10100023 10100024 10100027 10100029 ... 80101425

  print("Number of NAs:")

## [1] "Number of NAs:"

  print(sum(is.na(new.d$respondid)))

## [1] 0

METHODOLOGY

How survey was completed
- P=Paper
- O=Online complete

All data

st_css()

  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')

	n	%	val%
Paper	2976	83.7	83.7
Online	581	16.3	16.3
Total	3557	100.0	100.0

LA County

	n	%	val%
Paper	321	100	100
Online	0	0	0
Total	321	100	100

Northern CA

	n	%	val%
Paper	168	80	80
Online	42	20	20
Total	210	100	100

Greater CA

	n	%	val%
Paper	275	87.3	87.3
Online	40	12.7	12.7
Total	315	100.0	100.0

Detroit

	n	%	val%
Paper	281	78.9	78.9
Online	75	21.1	21.1
Total	356	100.0	100.0

Louisiana

	n	%	val%
Paper	553	94.5	94.5
Online	32	5.5	5.5
Total	585	100.0	100.0

Georgia

	n	%	val%
Paper	1366	77.9	77.9
Online	388	22.1	22.1
Total	1754	100.0	100.0

Michigan

	n	%	val%
Paper	12	75	75
Online	4	25	25
Total	16	100	100

A1

A1. In what month and year were you first diagnosed with prostate cancer?

All data

# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

A1:month diagnosed
	n	%	val%
0	1	0.0	0.0
0*	1	0.0	0.0
1	227	6.4	6.4
10	268	7.5	7.5
11	215	6.0	6.0
12	168	4.7	4.7
18	1	0.0	0.0
2	227	6.4	6.4
22	1	0.0	0.0
25	2	0.1	0.1
3	278	7.8	7.8
4	284	8.0	8.0
48	1	0.0	0.0
5	264	7.4	7.4
6	426	12.0	12.0
7	258	7.3	7.3
8	236	6.6	6.6
9	221	6.2	6.2
“NA”	478	13.4	13.4
Total	3557	100.0	100.0

  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")

A1:year diagnosed
	n	%	val%
1914	1	0.0	0.0
1915	3	0.1	0.1
1916	7	0.2	0.2
1917	9	0.3	0.3
1918	3	0.1	0.1
1937	1	0.0	0.0
1941	1	0.0	0.0
1943	1	0.0	0.0
1944	1	0.0	0.0
1945	2	0.1	0.1
1946	1	0.0	0.0
1947	2	0.1	0.1
1948	2	0.1	0.1
1949	2	0.1	0.1
1950	3	0.1	0.1
1951	2	0.1	0.1
1952	3	0.1	0.1
1953	1	0.0	0.0
1954	2	0.1	0.1
1955	2	0.1	0.1
1956	1	0.0	0.0
1963	2	0.1	0.1
1965	1	0.0	0.0
1987	1	0.0	0.0
1989	1	0.0	0.0
1990	1	0.0	0.0
1993	3	0.1	0.1
1994	1	0.0	0.0
1995	2	0.1	0.1
1996	1	0.0	0.0
1997	1	0.0	0.0
1998	4	0.1	0.1
1999	4	0.1	0.1
20	1	0.0	0.0
2000	1	0.0	0.0
2001	1	0.0	0.0
2002	1	0.0	0.0
2003	2	0.1	0.1
2004	6	0.2	0.2
2005	6	0.2	0.2
2006	5	0.1	0.1
2007	4	0.1	0.1
2008	8	0.2	0.2
2009	7	0.2	0.2
2010	17	0.5	0.5
2011	13	0.4	0.4
2012	29	0.8	0.8
2013	63	1.8	1.8
2014	177	5.0	5.0
2015	860	24.2	24.2
2016	1157	32.5	32.5
2017	551	15.5	15.5
2018	200	5.6	5.6
2019	52	1.5	1.5
2020	22	0.6	0.6
2021	5	0.1	0.1
615	1	0.0	0.0
“NA”	296	8.3	8.3
Total	3557	100.0	100.0

  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	10	0.3	0.3
HAVE_had_ProstateCancer	3547	99.7	99.7
Total	3557	100.0	100.0

LA County

A1:month diagnosed
	n	%	val%
0	1	0.3	0.3
0*	0	0.0	0.0
1	25	7.8	7.8
10	22	6.9	6.9
11	13	4.0	4.0
12	12	3.7	3.7
18	0	0.0	0.0
2	13	4.0	4.0
22	0	0.0	0.0
25	0	0.0	0.0
3	20	6.2	6.2
4	21	6.5	6.5
48	0	0.0	0.0
5	25	7.8	7.8
6	22	6.9	6.9
7	15	4.7	4.7
8	18	5.6	5.6
9	13	4.0	4.0
“NA”	101	31.5	31.5
Total	321	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	0	0.0	0.0
1916	0	0.0	0.0
1917	0	0.0	0.0
1918	0	0.0	0.0
1937	0	0.0	0.0
1941	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	0	0.0	0.0
1946	0	0.0	0.0
1947	0	0.0	0.0
1948	0	0.0	0.0
1949	0	0.0	0.0
1950	0	0.0	0.0
1951	0	0.0	0.0
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	0	0.0	0.0
1956	0	0.0	0.0
1963	0	0.0	0.0
1965	1	0.3	0.3
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	0	0.0	0.0
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	0	0.0	0.0
1999	0	0.0	0.0
20	0	0.0	0.0
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	0	0.0	0.0
2003	0	0.0	0.0
2004	0	0.0	0.0
2005	0	0.0	0.0
2006	1	0.3	0.3
2007	0	0.0	0.0
2008	2	0.6	0.6
2009	1	0.3	0.3
2010	1	0.3	0.3
2011	0	0.0	0.0
2012	0	0.0	0.0
2013	5	1.6	1.6
2014	8	2.5	2.5
2015	85	26.5	26.5
2016	122	38.0	38.0
2017	64	19.9	19.9
2018	19	5.9	5.9
2019	0	0.0	0.0
2020	1	0.3	0.3
2021	0	0.0	0.0
615	0	0.0	0.0
“NA”	11	3.4	3.4
Total	321	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	0	0	0
HAVE_had_ProstateCancer	321	100	100
Total	321	100	100

Northern CA

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	0	0.0	0.0
1	16	7.6	7.6
10	21	10.0	10.0
11	13	6.2	6.2
12	5	2.4	2.4
18	0	0.0	0.0
2	18	8.6	8.6
22	0	0.0	0.0
25	0	0.0	0.0
3	21	10.0	10.0
4	11	5.2	5.2
48	0	0.0	0.0
5	14	6.7	6.7
6	22	10.5	10.5
7	13	6.2	6.2
8	15	7.1	7.1
9	11	5.2	5.2
“NA”	30	14.3	14.3
Total	210	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	0	0.0	0.0
1916	1	0.5	0.5
1917	0	0.0	0.0
1918	0	0.0	0.0
1937	0	0.0	0.0
1941	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	0	0.0	0.0
1946	1	0.5	0.5
1947	0	0.0	0.0
1948	0	0.0	0.0
1949	0	0.0	0.0
1950	1	0.5	0.5
1951	0	0.0	0.0
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	0	0.0	0.0
1956	0	0.0	0.0
1963	0	0.0	0.0
1965	0	0.0	0.0
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	0	0.0	0.0
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	0	0.0	0.0
1999	0	0.0	0.0
20	0	0.0	0.0
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	0	0.0	0.0
2003	0	0.0	0.0
2004	0	0.0	0.0
2005	0	0.0	0.0
2006	1	0.5	0.5
2007	0	0.0	0.0
2008	0	0.0	0.0
2009	0	0.0	0.0
2010	0	0.0	0.0
2011	1	0.5	0.5
2012	3	1.4	1.4
2013	2	1.0	1.0
2014	8	3.8	3.8
2015	62	29.5	29.5
2016	62	29.5	29.5
2017	35	16.7	16.7
2018	6	2.9	2.9
2019	2	1.0	1.0
2020	1	0.5	0.5
2021	0	0.0	0.0
615	0	0.0	0.0
“NA”	24	11.4	11.4
Total	210	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	0	0	0
HAVE_had_ProstateCancer	210	100	100
Total	210	100	100

Greater CA

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	0	0.0	0.0
1	18	5.7	5.7
10	27	8.6	8.6
11	21	6.7	6.7
12	17	5.4	5.4
18	0	0.0	0.0
2	19	6.0	6.0
22	0	0.0	0.0
25	0	0.0	0.0
3	20	6.3	6.3
4	26	8.3	8.3
48	0	0.0	0.0
5	23	7.3	7.3
6	46	14.6	14.6
7	24	7.6	7.6
8	22	7.0	7.0
9	19	6.0	6.0
“NA”	33	10.5	10.5
Total	315	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	1	0.3	0.3
1916	0	0.0	0.0
1917	1	0.3	0.3
1918	1	0.3	0.3
1937	0	0.0	0.0
1941	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	1	0.3	0.3
1946	0	0.0	0.0
1947	0	0.0	0.0
1948	0	0.0	0.0
1949	0	0.0	0.0
1950	1	0.3	0.3
1951	1	0.3	0.3
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	0	0.0	0.0
1956	0	0.0	0.0
1963	0	0.0	0.0
1965	0	0.0	0.0
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	1	0.3	0.3
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	0	0.0	0.0
1999	0	0.0	0.0
20	0	0.0	0.0
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	1	0.3	0.3
2003	0	0.0	0.0
2004	1	0.3	0.3
2005	2	0.6	0.6
2006	0	0.0	0.0
2007	0	0.0	0.0
2008	0	0.0	0.0
2009	1	0.3	0.3
2010	4	1.3	1.3
2011	0	0.0	0.0
2012	1	0.3	0.3
2013	4	1.3	1.3
2014	18	5.7	5.7
2015	91	28.9	28.9
2016	123	39.0	39.0
2017	26	8.3	8.3
2018	7	2.2	2.2
2019	3	1.0	1.0
2020	1	0.3	0.3
2021	0	0.0	0.0
615	0	0.0	0.0
“NA”	25	7.9	7.9
Total	315	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	0	0	0
HAVE_had_ProstateCancer	315	100	100
Total	315	100	100

Detroit

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	0	0.0	0.0
1	26	7.3	7.3
10	21	5.9	5.9
11	16	4.5	4.5
12	19	5.3	5.3
18	1	0.3	0.3
2	18	5.1	5.1
22	0	0.0	0.0
25	0	0.0	0.0
3	33	9.3	9.3
4	27	7.6	7.6
48	0	0.0	0.0
5	19	5.3	5.3
6	50	14.0	14.0
7	28	7.9	7.9
8	25	7.0	7.0
9	26	7.3	7.3
“NA”	47	13.2	13.2
Total	356	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	1	0.3	0.3
1915	1	0.3	0.3
1916	1	0.3	0.3
1917	1	0.3	0.3
1918	1	0.3	0.3
1937	1	0.3	0.3
1941	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	0	0.0	0.0
1946	0	0.0	0.0
1947	1	0.3	0.3
1948	1	0.3	0.3
1949	0	0.0	0.0
1950	1	0.3	0.3
1951	0	0.0	0.0
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	1	0.3	0.3
1956	0	0.0	0.0
1963	2	0.6	0.6
1965	0	0.0	0.0
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	0	0.0	0.0
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	1	0.3	0.3
1999	1	0.3	0.3
20	1	0.3	0.3
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	0	0.0	0.0
2003	1	0.3	0.3
2004	1	0.3	0.3
2005	1	0.3	0.3
2006	0	0.0	0.0
2007	1	0.3	0.3
2008	0	0.0	0.0
2009	0	0.0	0.0
2010	2	0.6	0.6
2011	3	0.8	0.8
2012	4	1.1	1.1
2013	17	4.8	4.8
2014	26	7.3	7.3
2015	51	14.3	14.3
2016	66	18.5	18.5
2017	71	19.9	19.9
2018	48	13.5	13.5
2019	11	3.1	3.1
2020	1	0.3	0.3
2021	2	0.6	0.6
615	1	0.3	0.3
“NA”	34	9.6	9.6
Total	356	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	1	0.3	0.3
HAVE_had_ProstateCancer	355	99.7	99.7
Total	356	100.0	100.0

Louisiana

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	0	0.0	0.0
1	37	6.3	6.3
10	43	7.4	7.4
11	32	5.5	5.5
12	26	4.4	4.4
18	0	0.0	0.0
2	45	7.7	7.7
22	1	0.2	0.2
25	1	0.2	0.2
3	60	10.3	10.3
4	62	10.6	10.6
48	0	0.0	0.0
5	54	9.2	9.2
6	80	13.7	13.7
7	48	8.2	8.2
8	29	5.0	5.0
9	40	6.8	6.8
“NA”	27	4.6	4.6
Total	585	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	1	0.2	0.2
1916	0	0.0	0.0
1917	3	0.5	0.5
1918	0	0.0	0.0
1937	0	0.0	0.0
1941	0	0.0	0.0
1943	1	0.2	0.2
1944	0	0.0	0.0
1945	0	0.0	0.0
1946	0	0.0	0.0
1947	0	0.0	0.0
1948	0	0.0	0.0
1949	0	0.0	0.0
1950	0	0.0	0.0
1951	0	0.0	0.0
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	0	0.0	0.0
1956	0	0.0	0.0
1963	0	0.0	0.0
1965	0	0.0	0.0
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	0	0.0	0.0
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	1	0.2	0.2
1999	0	0.0	0.0
20	0	0.0	0.0
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	0	0.0	0.0
2003	0	0.0	0.0
2004	1	0.2	0.2
2005	0	0.0	0.0
2006	0	0.0	0.0
2007	0	0.0	0.0
2008	2	0.3	0.3
2009	0	0.0	0.0
2010	2	0.3	0.3
2011	2	0.3	0.3
2012	2	0.3	0.3
2013	5	0.9	0.9
2014	22	3.8	3.8
2015	98	16.8	16.8
2016	234	40.0	40.0
2017	141	24.1	24.1
2018	37	6.3	6.3
2019	13	2.2	2.2
2020	2	0.3	0.3
2021	0	0.0	0.0
615	0	0.0	0.0
“NA”	18	3.1	3.1
Total	585	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	5	0.9	0.9
HAVE_had_ProstateCancer	580	99.1	99.1
Total	585	100.0	100.0

Georgia

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	1	0.1	0.1
1	102	5.8	5.8
10	133	7.6	7.6
11	120	6.8	6.8
12	87	5.0	5.0
18	0	0.0	0.0
2	112	6.4	6.4
22	0	0.0	0.0
25	1	0.1	0.1
3	121	6.9	6.9
4	136	7.8	7.8
48	1	0.1	0.1
5	129	7.4	7.4
6	204	11.6	11.6
7	130	7.4	7.4
8	125	7.1	7.1
9	112	6.4	6.4
“NA”	240	13.7	13.7
Total	1754	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	0	0.0	0.0
1916	5	0.3	0.3
1917	4	0.2	0.2
1918	1	0.1	0.1
1937	0	0.0	0.0
1941	1	0.1	0.1
1943	0	0.0	0.0
1944	1	0.1	0.1
1945	1	0.1	0.1
1946	0	0.0	0.0
1947	1	0.1	0.1
1948	1	0.1	0.1
1949	2	0.1	0.1
1950	0	0.0	0.0
1951	1	0.1	0.1
1952	3	0.2	0.2
1953	1	0.1	0.1
1954	2	0.1	0.1
1955	1	0.1	0.1
1956	1	0.1	0.1
1963	0	0.0	0.0
1965	0	0.0	0.0
1987	1	0.1	0.1
1989	1	0.1	0.1
1990	1	0.1	0.1
1993	3	0.2	0.2
1994	0	0.0	0.0
1995	2	0.1	0.1
1996	1	0.1	0.1
1997	1	0.1	0.1
1998	2	0.1	0.1
1999	3	0.2	0.2
20	0	0.0	0.0
2000	1	0.1	0.1
2001	1	0.1	0.1
2002	0	0.0	0.0
2003	1	0.1	0.1
2004	3	0.2	0.2
2005	3	0.2	0.2
2006	3	0.2	0.2
2007	3	0.2	0.2
2008	4	0.2	0.2
2009	5	0.3	0.3
2010	7	0.4	0.4
2011	6	0.3	0.3
2012	17	1.0	1.0
2013	25	1.4	1.4
2014	90	5.1	5.1
2015	472	26.9	26.9
2016	550	31.4	31.4
2017	214	12.2	12.2
2018	83	4.7	4.7
2019	22	1.3	1.3
2020	16	0.9	0.9
2021	3	0.2	0.2
615	0	0.0	0.0
“NA”	184	10.5	10.5
Total	1754	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	4	0.2	0.2
HAVE_had_ProstateCancer	1750	99.8	99.8
Total	1754	100.0	100.0

Michigan

A1:month diagnosed
	n	%	val%
0	0	0.0	0.0
0*	0	0.0	0.0
1	3	18.8	18.8
10	1	6.2	6.2
11	0	0.0	0.0
12	2	12.5	12.5
18	0	0.0	0.0
2	2	12.5	12.5
22	0	0.0	0.0
25	0	0.0	0.0
3	3	18.8	18.8
4	1	6.2	6.2
48	0	0.0	0.0
5	0	0.0	0.0
6	2	12.5	12.5
7	0	0.0	0.0
8	2	12.5	12.5
9	0	0.0	0.0
“NA”	0	0.0	0.0
Total	16	100.0	100.0

A1:year diagnosed
	n	%	val%
1914	0	0.0	0.0
1915	0	0.0	0.0
1916	0	0.0	0.0
1917	0	0.0	0.0
1918	0	0.0	0.0
1937	0	0.0	0.0
1941	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	0	0.0	0.0
1946	0	0.0	0.0
1947	0	0.0	0.0
1948	0	0.0	0.0
1949	0	0.0	0.0
1950	0	0.0	0.0
1951	0	0.0	0.0
1952	0	0.0	0.0
1953	0	0.0	0.0
1954	0	0.0	0.0
1955	0	0.0	0.0
1956	0	0.0	0.0
1963	0	0.0	0.0
1965	0	0.0	0.0
1987	0	0.0	0.0
1989	0	0.0	0.0
1990	0	0.0	0.0
1993	0	0.0	0.0
1994	0	0.0	0.0
1995	0	0.0	0.0
1996	0	0.0	0.0
1997	0	0.0	0.0
1998	0	0.0	0.0
1999	0	0.0	0.0
20	0	0.0	0.0
2000	0	0.0	0.0
2001	0	0.0	0.0
2002	0	0.0	0.0
2003	0	0.0	0.0
2004	0	0.0	0.0
2005	0	0.0	0.0
2006	0	0.0	0.0
2007	0	0.0	0.0
2008	0	0.0	0.0
2009	0	0.0	0.0
2010	1	6.2	6.2
2011	1	6.2	6.2
2012	2	12.5	12.5
2013	5	31.2	31.2
2014	5	31.2	31.2
2015	1	6.2	6.2
2016	0	0.0	0.0
2017	0	0.0	0.0
2018	0	0.0	0.0
2019	1	6.2	6.2
2020	0	0.0	0.0
2021	0	0.0	0.0
615	0	0.0	0.0
“NA”	0	0.0	0.0
Total	16	100.0	100.0

A1:not diagnosed
	n	%	val%
NEVER_had_ProstateCancer	0	0	0
HAVE_had_ProstateCancer	16	100	100
Total	16	100	100

A2

A2. Do you identify as Black or African American?
- 2=Yes
- 1=No

All data

a2 <- as.factor(d[,"a2"])
levels(a2) <- list(No="1",
                   Yes="2",
                   Scantron_Error="*")
  a2 <- ordered(a2, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")

A2
	n	%	val%
Yes	3257	91.6	99.7
No	10	0.3	0.3
Scantron_Error	0	0.0	0.0
NA	290	8.2	NA
Total	3557	100.0	100.0

LA County

A2
	n	%	val%
Yes	303	94.4	100
No	0	0.0	0
Scantron_Error	0	0.0	0
NA	18	5.6	NA
Total	321	100.0	100

Northern CA

A2
	n	%	val%
Yes	195	92.9	100
No	0	0.0	0
Scantron_Error	0	0.0	0
NA	15	7.1	NA
Total	210	100.0	100

Greater CA

A2
	n	%	val%
Yes	289	91.7	99.3
No	2	0.6	0.7
Scantron_Error	0	0.0	0.0
NA	24	7.6	NA
Total	315	100.0	100.0

Detroit

A2
	n	%	val%
Yes	343	96.3	99.4
No	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	11	3.1	NA
Total	356	100.0	100.0

Louisiana

A2
	n	%	val%
Yes	526	89.9	99.2
No	4	0.7	0.8
Scantron_Error	0	0.0	0.0
NA	55	9.4	NA
Total	585	100.0	100.0

Georgia

A2
	n	%	val%
Yes	1586	90.4	99.9
No	2	0.1	0.1
Scantron_Error	0	0.0	0.0
NA	166	9.5	NA
Total	1754	100.0	100.0

Michigan

A2
	n	%	val%
Yes	15	93.8	100
No	0	0.0	0
Scantron_Error	0	0.0	0
NA	1	6.2	NA
Total	16	100.0	100

A4

A4. What is your month and year of birth?

All data

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")

A4: Month of birth
	n	%	val%
1	303	8.5	8.5
10	314	8.8	8.8
11	283	8.0	8.0
12	333	9.4	9.4
18	1	0.0	0.0
2	266	7.5	7.5
22	1	0.0	0.0
24	1	0.0	0.0
25	1	0.0	0.0
26	1	0.0	0.0
3	282	7.9	7.9
31	1	0.0	0.0
33	1	0.0	0.0
35	1	0.0	0.0
4	246	6.9	6.9
48	1	0.0	0.0
5	269	7.6	7.6
57	1	0.0	0.0
58	1	0.0	0.0
6	297	8.3	8.3
61	1	0.0	0.0
7	297	8.3	8.3
71	2	0.1	0.1
8	336	9.4	9.4
9	293	8.2	8.2
96	1	0.0	0.0
“NA”	23	0.6	0.6
Total	3557	100.0	100.0

#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")

A4: Year of birth
	n	%	val%
1062	1	0.0	0.0
1340	1	0.0	0.0
1930	1	0.0	0.0
1933	1	0.0	0.0
1936	1	0.0	0.0
1937	10	0.3	0.3
1938	10	0.3	0.3
1939	9	0.3	0.3
1940	19	0.5	0.5
1941	59	1.7	1.7
1942	63	1.8	1.8
1943	84	2.4	2.4
1944	85	2.4	2.4
1945	104	2.9	2.9
1946	145	4.1	4.1
1947	140	3.9	3.9
1948	192	5.4	5.4
1949	201	5.7	5.7
1950	208	5.8	5.8
1951	199	5.6	5.6
1952	187	5.3	5.3
1953	175	4.9	4.9
1954	180	5.1	5.1
1955	180	5.1	5.1
1956	191	5.4	5.4
1957	190	5.3	5.3
1958	133	3.7	3.7
1959	107	3.0	3.0
1960	147	4.1	4.1
1961	104	2.9	2.9
1962	79	2.2	2.2
1963	73	2.1	2.1
1964	57	1.6	1.6
1965	45	1.3	1.3
1966	37	1.0	1.0
1967	32	0.9	0.9
1968	24	0.7	0.7
1969	19	0.5	0.5
1970	13	0.4	0.4
1971	8	0.2	0.2
1972	3	0.1	0.1
1973	12	0.3	0.3
1974	1	0.0	0.0
1976	2	0.1	0.1
1977	1	0.0	0.0
1978	1	0.0	0.0
2015	3	0.1	0.1
2018	1	0.0	0.0
748	1	0.0	0.0
“NA”	18	0.5	0.5
Total	3557	100.0	100.0

LA County

A4: Month of birth
	n	%	val%
1	28	8.7	8.7
10	27	8.4	8.4
11	28	8.7	8.7
12	29	9.0	9.0
18	0	0.0	0.0
2	23	7.2	7.2
22	0	0.0	0.0
24	1	0.3	0.3
25	0	0.0	0.0
26	0	0.0	0.0
3	31	9.7	9.7
31	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
4	19	5.9	5.9
48	0	0.0	0.0
5	22	6.9	6.9
57	0	0.0	0.0
58	1	0.3	0.3
6	29	9.0	9.0
61	0	0.0	0.0
7	23	7.2	7.2
71	0	0.0	0.0
8	35	10.9	10.9
9	23	7.2	7.2
96	0	0.0	0.0
“NA”	2	0.6	0.6
Total	321	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	0	0.0	0.0
1936	0	0.0	0.0
1937	2	0.6	0.6
1938	1	0.3	0.3
1939	3	0.9	0.9
1940	0	0.0	0.0
1941	2	0.6	0.6
1942	8	2.5	2.5
1943	8	2.5	2.5
1944	6	1.9	1.9
1945	8	2.5	2.5
1946	14	4.4	4.4
1947	15	4.7	4.7
1948	19	5.9	5.9
1949	15	4.7	4.7
1950	17	5.3	5.3
1951	16	5.0	5.0
1952	21	6.5	6.5
1953	16	5.0	5.0
1954	18	5.6	5.6
1955	18	5.6	5.6
1956	18	5.6	5.6
1957	16	5.0	5.0
1958	10	3.1	3.1
1959	8	2.5	2.5
1960	10	3.1	3.1
1961	13	4.0	4.0
1962	5	1.6	1.6
1963	5	1.6	1.6
1964	3	0.9	0.9
1965	4	1.2	1.2
1966	8	2.5	2.5
1967	5	1.6	1.6
1968	3	0.9	0.9
1969	2	0.6	0.6
1970	0	0.0	0.0
1971	0	0.0	0.0
1972	0	0.0	0.0
1973	1	0.3	0.3
1974	0	0.0	0.0
1976	0	0.0	0.0
1977	0	0.0	0.0
1978	0	0.0	0.0
2015	1	0.3	0.3
2018	1	0.3	0.3
748	0	0.0	0.0
“NA”	1	0.3	0.3
Total	321	100.0	100.0

Northern CA

A4: Month of birth
	n	%	val%
1	14	6.7	6.7
10	20	9.5	9.5
11	23	11.0	11.0
12	27	12.9	12.9
18	0	0.0	0.0
2	11	5.2	5.2
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
3	15	7.1	7.1
31	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
4	16	7.6	7.6
48	0	0.0	0.0
5	16	7.6	7.6
57	0	0.0	0.0
58	0	0.0	0.0
6	20	9.5	9.5
61	0	0.0	0.0
7	13	6.2	6.2
71	0	0.0	0.0
8	16	7.6	7.6
9	18	8.6	8.6
96	0	0.0	0.0
“NA”	1	0.5	0.5
Total	210	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	0	0.0	0.0
1936	0	0.0	0.0
1937	1	0.5	0.5
1938	1	0.5	0.5
1939	0	0.0	0.0
1940	1	0.5	0.5
1941	2	1.0	1.0
1942	8	3.8	3.8
1943	3	1.4	1.4
1944	3	1.4	1.4
1945	6	2.9	2.9
1946	11	5.2	5.2
1947	12	5.7	5.7
1948	8	3.8	3.8
1949	14	6.7	6.7
1950	13	6.2	6.2
1951	18	8.6	8.6
1952	10	4.8	4.8
1953	11	5.2	5.2
1954	13	6.2	6.2
1955	3	1.4	1.4
1956	5	2.4	2.4
1957	10	4.8	4.8
1958	16	7.6	7.6
1959	8	3.8	3.8
1960	9	4.3	4.3
1961	3	1.4	1.4
1962	1	0.5	0.5
1963	3	1.4	1.4
1964	3	1.4	1.4
1965	2	1.0	1.0
1966	2	1.0	1.0
1967	2	1.0	1.0
1968	1	0.5	0.5
1969	4	1.9	1.9
1970	1	0.5	0.5
1971	0	0.0	0.0
1972	0	0.0	0.0
1973	0	0.0	0.0
1974	0	0.0	0.0
1976	1	0.5	0.5
1977	0	0.0	0.0
1978	0	0.0	0.0
2015	0	0.0	0.0
2018	0	0.0	0.0
748	0	0.0	0.0
“NA”	1	0.5	0.5
Total	210	100.0	100.0

Greater CA

A4: Month of birth
	n	%	val%
1	24	7.6	7.6
10	26	8.3	8.3
11	28	8.9	8.9
12	22	7.0	7.0
18	0	0.0	0.0
2	25	7.9	7.9
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
3	18	5.7	5.7
31	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
4	19	6.0	6.0
48	0	0.0	0.0
5	31	9.8	9.8
57	0	0.0	0.0
58	0	0.0	0.0
6	29	9.2	9.2
61	0	0.0	0.0
7	38	12.1	12.1
71	0	0.0	0.0
8	29	9.2	9.2
9	22	7.0	7.0
96	0	0.0	0.0
“NA”	4	1.3	1.3
Total	315	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	0	0.0	0.0
1936	0	0.0	0.0
1937	0	0.0	0.0
1938	0	0.0	0.0
1939	0	0.0	0.0
1940	0	0.0	0.0
1941	6	1.9	1.9
1942	6	1.9	1.9
1943	6	1.9	1.9
1944	4	1.3	1.3
1945	13	4.1	4.1
1946	14	4.4	4.4
1947	11	3.5	3.5
1948	15	4.8	4.8
1949	24	7.6	7.6
1950	17	5.4	5.4
1951	17	5.4	5.4
1952	11	3.5	3.5
1953	15	4.8	4.8
1954	20	6.3	6.3
1955	17	5.4	5.4
1956	13	4.1	4.1
1957	14	4.4	4.4
1958	9	2.9	2.9
1959	9	2.9	2.9
1960	22	7.0	7.0
1961	11	3.5	3.5
1962	8	2.5	2.5
1963	10	3.2	3.2
1964	2	0.6	0.6
1965	8	2.5	2.5
1966	0	0.0	0.0
1967	2	0.6	0.6
1968	1	0.3	0.3
1969	2	0.6	0.6
1970	0	0.0	0.0
1971	1	0.3	0.3
1972	0	0.0	0.0
1973	1	0.3	0.3
1974	0	0.0	0.0
1976	0	0.0	0.0
1977	0	0.0	0.0
1978	1	0.3	0.3
2015	0	0.0	0.0
2018	0	0.0	0.0
748	1	0.3	0.3
“NA”	4	1.3	1.3
Total	315	100.0	100.0

Detroit

A4: Month of birth
	n	%	val%
1	28	7.9	7.9
10	36	10.1	10.1
11	24	6.7	6.7
12	23	6.5	6.5
18	1	0.3	0.3
2	28	7.9	7.9
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
3	24	6.7	6.7
31	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
4	31	8.7	8.7
48	0	0.0	0.0
5	21	5.9	5.9
57	0	0.0	0.0
58	0	0.0	0.0
6	34	9.6	9.6
61	0	0.0	0.0
7	29	8.1	8.1
71	1	0.3	0.3
8	40	11.2	11.2
9	36	10.1	10.1
96	0	0.0	0.0
“NA”	0	0.0	0.0
Total	356	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	1	0.3	0.3
1936	0	0.0	0.0
1937	4	1.1	1.1
1938	1	0.3	0.3
1939	3	0.8	0.8
1940	2	0.6	0.6
1941	8	2.2	2.2
1942	7	2.0	2.0
1943	9	2.5	2.5
1944	8	2.2	2.2
1945	9	2.5	2.5
1946	11	3.1	3.1
1947	18	5.1	5.1
1948	9	2.5	2.5
1949	14	3.9	3.9
1950	18	5.1	5.1
1951	16	4.5	4.5
1952	15	4.2	4.2
1953	11	3.1	3.1
1954	17	4.8	4.8
1955	26	7.3	7.3
1956	26	7.3	7.3
1957	27	7.6	7.6
1958	16	4.5	4.5
1959	8	2.2	2.2
1960	14	3.9	3.9
1961	12	3.4	3.4
1962	14	3.9	3.9
1963	8	2.2	2.2
1964	4	1.1	1.1
1965	3	0.8	0.8
1966	4	1.1	1.1
1967	3	0.8	0.8
1968	1	0.3	0.3
1969	3	0.8	0.8
1970	2	0.6	0.6
1971	1	0.3	0.3
1972	0	0.0	0.0
1973	2	0.6	0.6
1974	0	0.0	0.0
1976	0	0.0	0.0
1977	1	0.3	0.3
1978	0	0.0	0.0
2015	0	0.0	0.0
2018	0	0.0	0.0
748	0	0.0	0.0
“NA”	0	0.0	0.0
Total	356	100.0	100.0

Louisiana

A4: Month of birth
	n	%	val%
1	45	7.7	7.7
10	57	9.7	9.7
11	50	8.5	8.5
12	61	10.4	10.4
18	0	0.0	0.0
2	47	8.0	8.0
22	0	0.0	0.0
24	0	0.0	0.0
25	1	0.2	0.2
26	0	0.0	0.0
3	40	6.8	6.8
31	1	0.2	0.2
33	0	0.0	0.0
35	0	0.0	0.0
4	37	6.3	6.3
48	0	0.0	0.0
5	31	5.3	5.3
57	0	0.0	0.0
58	0	0.0	0.0
6	49	8.4	8.4
61	0	0.0	0.0
7	57	9.7	9.7
71	0	0.0	0.0
8	49	8.4	8.4
9	55	9.4	9.4
96	0	0.0	0.0
“NA”	5	0.9	0.9
Total	585	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	0	0.0	0.0
1936	1	0.2	0.2
1937	3	0.5	0.5
1938	7	1.2	1.2
1939	2	0.3	0.3
1940	9	1.5	1.5
1941	12	2.1	2.1
1942	6	1.0	1.0
1943	13	2.2	2.2
1944	14	2.4	2.4
1945	21	3.6	3.6
1946	16	2.7	2.7
1947	23	3.9	3.9
1948	31	5.3	5.3
1949	33	5.6	5.6
1950	31	5.3	5.3
1951	31	5.3	5.3
1952	28	4.8	4.8
1953	45	7.7	7.7
1954	25	4.3	4.3
1955	29	5.0	5.0
1956	38	6.5	6.5
1957	30	5.1	5.1
1958	19	3.2	3.2
1959	17	2.9	2.9
1960	19	3.2	3.2
1961	20	3.4	3.4
1962	13	2.2	2.2
1963	14	2.4	2.4
1964	9	1.5	1.5
1965	5	0.9	0.9
1966	3	0.5	0.5
1967	7	1.2	1.2
1968	2	0.3	0.3
1969	1	0.2	0.2
1970	2	0.3	0.3
1971	1	0.2	0.2
1972	1	0.2	0.2
1973	0	0.0	0.0
1974	0	0.0	0.0
1976	0	0.0	0.0
1977	0	0.0	0.0
1978	0	0.0	0.0
2015	1	0.2	0.2
2018	0	0.0	0.0
748	0	0.0	0.0
“NA”	3	0.5	0.5
Total	585	100.0	100.0

Georgia

A4: Month of birth
	n	%	val%
1	163	9.3	9.3
10	146	8.3	8.3
11	130	7.4	7.4
12	169	9.6	9.6
18	0	0.0	0.0
2	130	7.4	7.4
22	1	0.1	0.1
24	0	0.0	0.0
25	0	0.0	0.0
26	1	0.1	0.1
3	154	8.8	8.8
31	0	0.0	0.0
33	1	0.1	0.1
35	1	0.1	0.1
4	121	6.9	6.9
48	1	0.1	0.1
5	147	8.4	8.4
57	1	0.1	0.1
58	0	0.0	0.0
6	135	7.7	7.7
61	1	0.1	0.1
7	135	7.7	7.7
71	1	0.1	0.1
8	166	9.5	9.5
9	138	7.9	7.9
96	1	0.1	0.1
“NA”	11	0.6	0.6
Total	1754	100.0	100.0

A4: Year of birth
	n	%	val%
1062	1	0.1	0.1
1340	1	0.1	0.1
1930	1	0.1	0.1
1933	0	0.0	0.0
1936	0	0.0	0.0
1937	0	0.0	0.0
1938	0	0.0	0.0
1939	1	0.1	0.1
1940	7	0.4	0.4
1941	29	1.7	1.7
1942	28	1.6	1.6
1943	45	2.6	2.6
1944	50	2.9	2.9
1945	46	2.6	2.6
1946	79	4.5	4.5
1947	61	3.5	3.5
1948	109	6.2	6.2
1949	99	5.6	5.6
1950	111	6.3	6.3
1951	101	5.8	5.8
1952	101	5.8	5.8
1953	76	4.3	4.3
1954	86	4.9	4.9
1955	86	4.9	4.9
1956	88	5.0	5.0
1957	93	5.3	5.3
1958	63	3.6	3.6
1959	56	3.2	3.2
1960	72	4.1	4.1
1961	45	2.6	2.6
1962	37	2.1	2.1
1963	33	1.9	1.9
1964	36	2.1	2.1
1965	23	1.3	1.3
1966	19	1.1	1.1
1967	13	0.7	0.7
1968	16	0.9	0.9
1969	7	0.4	0.4
1970	8	0.5	0.5
1971	5	0.3	0.3
1972	2	0.1	0.1
1973	8	0.5	0.5
1974	1	0.1	0.1
1976	1	0.1	0.1
1977	0	0.0	0.0
1978	0	0.0	0.0
2015	1	0.1	0.1
2018	0	0.0	0.0
748	0	0.0	0.0
“NA”	9	0.5	0.5
Total	1754	100.0	100.0

Michigan

A4: Month of birth
	n	%	val%
1	1	6.2	6.2
10	2	12.5	12.5
11	0	0.0	0.0
12	2	12.5	12.5
18	0	0.0	0.0
2	2	12.5	12.5
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
3	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
4	3	18.8	18.8
48	0	0.0	0.0
5	1	6.2	6.2
57	0	0.0	0.0
58	0	0.0	0.0
6	1	6.2	6.2
61	0	0.0	0.0
7	2	12.5	12.5
71	0	0.0	0.0
8	1	6.2	6.2
9	1	6.2	6.2
96	0	0.0	0.0
“NA”	0	0.0	0.0
Total	16	100.0	100.0

A4: Year of birth
	n	%	val%
1062	0	0.0	0.0
1340	0	0.0	0.0
1930	0	0.0	0.0
1933	0	0.0	0.0
1936	0	0.0	0.0
1937	0	0.0	0.0
1938	0	0.0	0.0
1939	0	0.0	0.0
1940	0	0.0	0.0
1941	0	0.0	0.0
1942	0	0.0	0.0
1943	0	0.0	0.0
1944	0	0.0	0.0
1945	1	6.2	6.2
1946	0	0.0	0.0
1947	0	0.0	0.0
1948	1	6.2	6.2
1949	2	12.5	12.5
1950	1	6.2	6.2
1951	0	0.0	0.0
1952	1	6.2	6.2
1953	1	6.2	6.2
1954	1	6.2	6.2
1955	1	6.2	6.2
1956	3	18.8	18.8
1957	0	0.0	0.0
1958	0	0.0	0.0
1959	1	6.2	6.2
1960	1	6.2	6.2
1961	0	0.0	0.0
1962	1	6.2	6.2
1963	0	0.0	0.0
1964	0	0.0	0.0
1965	0	0.0	0.0
1966	1	6.2	6.2
1967	0	0.0	0.0
1968	0	0.0	0.0
1969	0	0.0	0.0
1970	0	0.0	0.0
1971	0	0.0	0.0
1972	0	0.0	0.0
1973	0	0.0	0.0
1974	0	0.0	0.0
1976	0	0.0	0.0
1977	0	0.0	0.0
1978	0	0.0	0.0
2015	0	0.0	0.0
2018	0	0.0	0.0
748	0	0.0	0.0
“NA”	0	0.0	0.0
Total	16	100.0	100.0

A5

A5. Where were you born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other

All data

a5 <- as.factor(d[,"a5"])
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")

A5: Where were you born?
	n	%	val%
US	3310	93.1	93.3
Africa	101	2.8	2.8
Cuba_Caribbean	84	2.4	2.4
Other	40	1.1	1.1
Scantron_Error	11	0.3	0.3
NA	11	0.3	NA
Total	3557	100.0	100.0

#a5: other
  a5other <- as.factor(d[,"a5other"])
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "Born place other")
  temp.d <- data.frame (new.d, a5other) 
  
  result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5 other: Where were you born?")

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	1	0.0	0.0
Barbados	1	0.0	0.0
Beaumont Texas	1	0.0	0.0
Belize	1	0.0	0.0
Belize Central America	1	0.0	0.0
Belize, Central America	2	0.1	0.1
Buffalo NY	1	0.0	0.0
Central America (Panama)	1	0.0	0.0
Central America Belize	1	0.0	0.0
Chatroux, France	1	0.0	0.0
Chicago IL	1	0.0	0.0
England	1	0.0	0.0
Ethiopia	1	0.0	0.0
Georgia Monroe Walton County	1	0.0	0.0
Germany	2	0.1	0.1
Ghana	2	0.1	0.1
Guinea-CKY	1	0.0	0.0
Guyana	4	0.1	0.1
Guyana: South America	1	0.0	0.0
Haiti	6	0.2	0.2
Heidleburg Germany	1	0.0	0.0
Jackson, Miss.	1	0.0	0.0
Jamaica	9	0.3	0.3
JAMAICA	1	0.0	0.0
Jamaica WI	1	0.0	0.0
Jamaican	1	0.0	0.0
Japan	1	0.0	0.0
Kientra, Morocco	1	0.0	0.0
Kingston, Jamaica	1	0.0	0.0
Liberia	1	0.0	0.0
London England	1	0.0	0.0
Macon County	1	0.0	0.0
Mississippi	1	0.0	0.0
“NA”	3485	98.0	98.0
Nassau Bahamas	2	0.1	0.1
New Orleans, LA	1	0.0	0.0
Nigeria	2	0.1	0.1
Nigeria.	1	0.0	0.0
Panama	1	0.0	0.0
Panama City of Panama	1	0.0	0.0
San Diego, CA	1	0.0	0.0
Sierre Leone	1	0.0	0.0
Southampton, Bermuda	1	0.0	0.0
Trinidad	1	0.0	0.0
Trinidad and Tobago	1	0.0	0.0
Trinidad.	1	0.0	0.0
UK	1	0.0	0.0
United States Texas	1	0.0	0.0
Upson County, GA	1	0.0	0.0
Venezuela	1	0.0	0.0
Venezuelan	1	0.0	0.0
West Indies	1	0.0	0.0
Total	3557	100.0	100.0

LA County

A5: Where were you born?
	n	%	val%
US	293	91.3	92.1
Africa	10	3.1	3.1
Cuba_Caribbean	9	2.8	2.8
Other	4	1.2	1.3
Scantron_Error	2	0.6	0.6
NA	3	0.9	NA
Total	321	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Beaumont Texas	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Buffalo NY	1	0.3	0.3
Central America (Panama)	0	0.0	0.0
Central America Belize	0	0.0	0.0
Chatroux, France	0	0.0	0.0
Chicago IL	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guinea-CKY	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	2	0.6	0.6
Heidleburg Germany	0	0.0	0.0
Jackson, Miss.	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Japan	0	0.0	0.0
Kientra, Morocco	0	0.0	0.0
Kingston, Jamaica	0	0.0	0.0
Liberia	0	0.0	0.0
London England	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	1	0.3	0.3
“NA”	311	96.9	96.9
Nassau Bahamas	0	0.0	0.0
New Orleans, LA	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Panama	1	0.3	0.3
Panama City of Panama	1	0.3	0.3
San Diego, CA	0	0.0	0.0
Sierre Leone	1	0.3	0.3
Southampton, Bermuda	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	1	0.3	0.3
UK	1	0.3	0.3
United States Texas	0	0.0	0.0
Upson County, GA	0	0.0	0.0
Venezuela	0	0.0	0.0
Venezuelan	0	0.0	0.0
West Indies	0	0.0	0.0
Total	321	100.0	100.0

Northern CA

A5: Where were you born?
	n	%	val%
US	192	91.4	91.4
Africa	12	5.7	5.7
Cuba_Caribbean	3	1.4	1.4
Other	3	1.4	1.4
Scantron_Error	0	0.0	0.0
Total	210	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	1	0.5	0.5
Barbados	0	0.0	0.0
Beaumont Texas	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Central America Belize	0	0.0	0.0
Chatroux, France	1	0.5	0.5
Chicago IL	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guinea-CKY	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Heidleburg Germany	0	0.0	0.0
Jackson, Miss.	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Japan	0	0.0	0.0
Kientra, Morocco	0	0.0	0.0
Kingston, Jamaica	0	0.0	0.0
Liberia	0	0.0	0.0
London England	1	0.5	0.5
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
“NA”	206	98.1	98.1
Nassau Bahamas	0	0.0	0.0
New Orleans, LA	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	1	0.5	0.5
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
San Diego, CA	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Southampton, Bermuda	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
UK	0	0.0	0.0
United States Texas	0	0.0	0.0
Upson County, GA	0	0.0	0.0
Venezuela	0	0.0	0.0
Venezuelan	0	0.0	0.0
West Indies	0	0.0	0.0
Total	210	100.0	100.0

Greater CA

A5: Where were you born?
	n	%	val%
US	275	87.3	87.9
Africa	23	7.3	7.3
Cuba_Caribbean	4	1.3	1.3
Other	8	2.5	2.6
Scantron_Error	3	1.0	1.0
NA	2	0.6	NA
Total	315	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Beaumont Texas	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Buffalo NY	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Central America Belize	0	0.0	0.0
Chatroux, France	0	0.0	0.0
Chicago IL	0	0.0	0.0
England	1	0.3	0.3
Ethiopia	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	1	0.3	0.3
Ghana	1	0.3	0.3
Guinea-CKY	0	0.0	0.0
Guyana	1	0.3	0.3
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Heidleburg Germany	0	0.0	0.0
Jackson, Miss.	1	0.3	0.3
Jamaica	1	0.3	0.3
JAMAICA	0	0.0	0.0
Jamaica WI	1	0.3	0.3
Jamaican	1	0.3	0.3
Japan	0	0.0	0.0
Kientra, Morocco	1	0.3	0.3
Kingston, Jamaica	0	0.0	0.0
Liberia	0	0.0	0.0
London England	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
“NA”	301	95.6	95.6
Nassau Bahamas	0	0.0	0.0
New Orleans, LA	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
San Diego, CA	1	0.3	0.3
Sierre Leone	0	0.0	0.0
Southampton, Bermuda	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
UK	0	0.0	0.0
United States Texas	1	0.3	0.3
Upson County, GA	0	0.0	0.0
Venezuela	1	0.3	0.3
Venezuelan	1	0.3	0.3
West Indies	0	0.0	0.0
Total	315	100.0	100.0

Detroit

A5: Where were you born?
	n	%	val%
US	353	99.2	99.4
Africa	1	0.3	0.3
Cuba_Caribbean	1	0.3	0.3
Other	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	0.3	NA
Total	356	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0	0
Barbados	0	0	0
Beaumont Texas	0	0	0
Belize	0	0	0
Belize Central America	0	0	0
Belize, Central America	0	0	0
Buffalo NY	0	0	0
Central America (Panama)	0	0	0
Central America Belize	0	0	0
Chatroux, France	0	0	0
Chicago IL	0	0	0
England	0	0	0
Ethiopia	0	0	0
Georgia Monroe Walton County	0	0	0
Germany	0	0	0
Ghana	0	0	0
Guinea-CKY	0	0	0
Guyana	0	0	0
Guyana: South America	0	0	0
Haiti	0	0	0
Heidleburg Germany	0	0	0
Jackson, Miss.	0	0	0
Jamaica	0	0	0
JAMAICA	0	0	0
Jamaica WI	0	0	0
Jamaican	0	0	0
Japan	0	0	0
Kientra, Morocco	0	0	0
Kingston, Jamaica	0	0	0
Liberia	0	0	0
London England	0	0	0
Macon County	0	0	0
Mississippi	0	0	0
“NA”	356	100	100
Nassau Bahamas	0	0	0
New Orleans, LA	0	0	0
Nigeria	0	0	0
Nigeria.	0	0	0
Panama	0	0	0
Panama City of Panama	0	0	0
San Diego, CA	0	0	0
Sierre Leone	0	0	0
Southampton, Bermuda	0	0	0
Trinidad	0	0	0
Trinidad and Tobago	0	0	0
Trinidad.	0	0	0
UK	0	0	0
United States Texas	0	0	0
Upson County, GA	0	0	0
Venezuela	0	0	0
Venezuelan	0	0	0
West Indies	0	0	0
Total	356	100	100

Louisiana

A5: Where were you born?
	n	%	val%
US	575	98.3	98.5
Africa	2	0.3	0.3
Cuba_Caribbean	6	1.0	1.0
Other	0	0.0	0.0
Scantron_Error	1	0.2	0.2
NA	1	0.2	NA
Total	585	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Beaumont Texas	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Central America Belize	0	0.0	0.0
Chatroux, France	0	0.0	0.0
Chicago IL	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guinea-CKY	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Heidleburg Germany	0	0.0	0.0
Jackson, Miss.	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Japan	0	0.0	0.0
Kientra, Morocco	0	0.0	0.0
Kingston, Jamaica	0	0.0	0.0
Liberia	0	0.0	0.0
London England	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
“NA”	584	99.8	99.8
Nassau Bahamas	0	0.0	0.0
New Orleans, LA	1	0.2	0.2
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
San Diego, CA	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Southampton, Bermuda	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
UK	0	0.0	0.0
United States Texas	0	0.0	0.0
Upson County, GA	0	0.0	0.0
Venezuela	0	0.0	0.0
Venezuelan	0	0.0	0.0
West Indies	0	0.0	0.0
Total	585	100.0	100.0

Georgia

A5: Where were you born?
	n	%	val%
US	1607	91.6	91.8
Africa	53	3.0	3.0
Cuba_Caribbean	60	3.4	3.4
Other	25	1.4	1.4
Scantron_Error	5	0.3	0.3
NA	4	0.2	NA
Total	1754	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	1	0.1	0.1
Beaumont Texas	1	0.1	0.1
Belize	1	0.1	0.1
Belize Central America	1	0.1	0.1
Belize, Central America	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Central America (Panama)	1	0.1	0.1
Central America Belize	1	0.1	0.1
Chatroux, France	0	0.0	0.0
Chicago IL	1	0.1	0.1
England	0	0.0	0.0
Ethiopia	1	0.1	0.1
Georgia Monroe Walton County	1	0.1	0.1
Germany	1	0.1	0.1
Ghana	1	0.1	0.1
Guinea-CKY	1	0.1	0.1
Guyana	3	0.2	0.2
Guyana: South America	1	0.1	0.1
Haiti	4	0.2	0.2
Heidleburg Germany	1	0.1	0.1
Jackson, Miss.	0	0.0	0.0
Jamaica	8	0.5	0.5
JAMAICA	1	0.1	0.1
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Japan	1	0.1	0.1
Kientra, Morocco	0	0.0	0.0
Kingston, Jamaica	1	0.1	0.1
Liberia	1	0.1	0.1
London England	0	0.0	0.0
Macon County	1	0.1	0.1
Mississippi	0	0.0	0.0
“NA”	1711	97.5	97.5
Nassau Bahamas	2	0.1	0.1
New Orleans, LA	0	0.0	0.0
Nigeria	2	0.1	0.1
Nigeria.	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
San Diego, CA	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Southampton, Bermuda	1	0.1	0.1
Trinidad	1	0.1	0.1
Trinidad and Tobago	1	0.1	0.1
Trinidad.	0	0.0	0.0
UK	0	0.0	0.0
United States Texas	0	0.0	0.0
Upson County, GA	1	0.1	0.1
Venezuela	0	0.0	0.0
Venezuelan	0	0.0	0.0
West Indies	1	0.1	0.1
Total	1754	100.0	100.0

Michigan

A5: Where were you born?
	n	%	val%
US	15	93.8	93.8
Africa	0	0.0	0.0
Cuba_Caribbean	1	6.2	6.2
Other	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

A5 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0	0
Barbados	0	0	0
Beaumont Texas	0	0	0
Belize	0	0	0
Belize Central America	0	0	0
Belize, Central America	0	0	0
Buffalo NY	0	0	0
Central America (Panama)	0	0	0
Central America Belize	0	0	0
Chatroux, France	0	0	0
Chicago IL	0	0	0
England	0	0	0
Ethiopia	0	0	0
Georgia Monroe Walton County	0	0	0
Germany	0	0	0
Ghana	0	0	0
Guinea-CKY	0	0	0
Guyana	0	0	0
Guyana: South America	0	0	0
Haiti	0	0	0
Heidleburg Germany	0	0	0
Jackson, Miss.	0	0	0
Jamaica	0	0	0
JAMAICA	0	0	0
Jamaica WI	0	0	0
Jamaican	0	0	0
Japan	0	0	0
Kientra, Morocco	0	0	0
Kingston, Jamaica	0	0	0
Liberia	0	0	0
London England	0	0	0
Macon County	0	0	0
Mississippi	0	0	0
“NA”	16	100	100
Nassau Bahamas	0	0	0
New Orleans, LA	0	0	0
Nigeria	0	0	0
Nigeria.	0	0	0
Panama	0	0	0
Panama City of Panama	0	0	0
San Diego, CA	0	0	0
Sierre Leone	0	0	0
Southampton, Bermuda	0	0	0
Trinidad	0	0	0
Trinidad and Tobago	0	0	0
Trinidad.	0	0	0
UK	0	0	0
United States Texas	0	0	0
Upson County, GA	0	0	0
Venezuela	0	0	0
Venezuelan	0	0	0
West Indies	0	0	0
Total	16	100	100

A6

A6. Where were you born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other

All data

a6 <- as.factor(d[,"a6"])
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")

a6: Where were you born?
	n	%	val%
US	3271	92.0	92.8
Africa	101	2.8	2.9
Cuba_Caribbean	95	2.7	2.7
Other	51	1.4	1.4
Scantron_Error	6	0.2	0.2
NA	33	0.9	NA
Total	3557	100.0	100.0

#a6: other
  a6other <- as.factor(d[,"a6other"])
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "Born place other")
  temp.d <- data.frame (new.d, a6other) 
  
  result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6 other: Where were you born?")

a6 other: Where were you born?
	n	%	val%
Barbados	1	0.0	0.0
Belize	1	0.0	0.0
Belize Central America	1	0.0	0.0
Belize, Central America	2	0.1	0.1
Biological father unknown	1	0.0	0.0
Blackstone, VA	1	0.0	0.0
Bombay.	1	0.0	0.0
British Honduras.	1	0.0	0.0
Canada	1	0.0	0.0
Canada.	1	0.0	0.0
Central America (Panama)	1	0.0	0.0
Cleveland Mississippi	1	0.0	0.0
Dead	1	0.0	0.0
Don’t know	2	0.1	0.1
East Pakistan	1	0.0	0.0
England	1	0.0	0.0
Ethiopia	1	0.0	0.0
Father’s birthplace is unknown.	1	0.0	0.0
Georgia Monroe Walton County	1	0.0	0.0
Germany	1	0.0	0.0
Ghana	1	0.0	0.0
Guyana	4	0.1	0.1
Guyana: South America	1	0.0	0.0
Haiti	6	0.2	0.2
Honduras, Central America	1	0.0	0.0
I don’t know	1	0.0	0.0
I was adopted, no info	1	0.0	0.0
Jamaica	11	0.3	0.3
JAMAICA	1	0.0	0.0
Jamaica WI	1	0.0	0.0
Jamaican	1	0.0	0.0
Macon County	1	0.0	0.0
Mississippi	2	0.1	0.1
Montserrat British VI	1	0.0	0.0
“NA”	3478	97.8	97.8
Nassau Bahamas	2	0.1	0.1
Never knew my father	1	0.0	0.0
Nigeria	2	0.1	0.1
Nigeria.	1	0.0	0.0
Not known	1	0.0	0.0
Panama	2	0.1	0.1
Panama-Central America	1	0.0	0.0
Panama Canal Zone	1	0.0	0.0
Panama City of Panama	1	0.0	0.0
Sierre Leone	1	0.0	0.0
Trinidad	1	0.0	0.0
Trinidad and Tobago	1	0.0	0.0
Trinidad.	1	0.0	0.0
United S Texas	1	0.0	0.0
Unknown	4	0.1	0.1
Venezuela	1	0.0	0.0
west Indies	1	0.0	0.0
Yatesville GA	1	0.0	0.0
Total	3557	100.0	100.0

LA County

a6: Where were you born?
	n	%	val%
US	293	91.3	91.6
Africa	10	3.1	3.1
Cuba_Caribbean	10	3.1	3.1
Other	5	1.6	1.6
Scantron_Error	2	0.6	0.6
NA	1	0.3	NA
Total	321	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Biological father unknown	0	0.0	0.0
Blackstone, VA	0	0.0	0.0
Bombay.	0	0.0	0.0
British Honduras.	0	0.0	0.0
Canada	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Cleveland Mississippi	1	0.3	0.3
Dead	0	0.0	0.0
Don’t know	0	0.0	0.0
East Pakistan	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Father’s birthplace is unknown.	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	2	0.6	0.6
Honduras, Central America	0	0.0	0.0
I don’t know	0	0.0	0.0
I was adopted, no info	0	0.0	0.0
Jamaica	2	0.6	0.6
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	311	96.9	96.9
Nassau Bahamas	0	0.0	0.0
Never knew my father	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Not known	0	0.0	0.0
Panama	0	0.0	0.0
Panama-Central America	0	0.0	0.0
Panama Canal Zone	0	0.0	0.0
Panama City of Panama	1	0.3	0.3
Sierre Leone	1	0.3	0.3
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	1	0.3	0.3
United S Texas	0	0.0	0.0
Unknown	1	0.3	0.3
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Yatesville GA	0	0.0	0.0
Total	321	100.0	100.0

Northern CA

a6: Where were you born?
	n	%	val%
US	189	90.0	91.3
Africa	12	5.7	5.8
Cuba_Caribbean	4	1.9	1.9
Other	2	1.0	1.0
Scantron_Error	0	0.0	0.0
NA	3	1.4	NA
Total	210	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Biological father unknown	0	0.0	0.0
Blackstone, VA	0	0.0	0.0
Bombay.	1	0.5	0.5
British Honduras.	0	0.0	0.0
Canada	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Cleveland Mississippi	0	0.0	0.0
Dead	0	0.0	0.0
Don’t know	0	0.0	0.0
East Pakistan	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Father’s birthplace is unknown.	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Honduras, Central America	0	0.0	0.0
I don’t know	0	0.0	0.0
I was adopted, no info	0	0.0	0.0
Jamaica	1	0.5	0.5
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	205	97.6	97.6
Nassau Bahamas	0	0.0	0.0
Never knew my father	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	1	0.5	0.5
Not known	0	0.0	0.0
Panama	1	0.5	0.5
Panama-Central America	0	0.0	0.0
Panama Canal Zone	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
United S Texas	0	0.0	0.0
Unknown	1	0.5	0.5
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Yatesville GA	0	0.0	0.0
Total	210	100.0	100.0

Greater CA

a6: Where were you born?
	n	%	val%
US	270	85.7	87.1
Africa	23	7.3	7.4
Cuba_Caribbean	7	2.2	2.3
Other	9	2.9	2.9
Scantron_Error	1	0.3	0.3
NA	5	1.6	NA
Total	315	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Biological father unknown	0	0.0	0.0
Blackstone, VA	1	0.3	0.3
Bombay.	0	0.0	0.0
British Honduras.	0	0.0	0.0
Canada	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Cleveland Mississippi	0	0.0	0.0
Dead	0	0.0	0.0
Don’t know	1	0.3	0.3
East Pakistan	0	0.0	0.0
England	1	0.3	0.3
Ethiopia	0	0.0	0.0
Father’s birthplace is unknown.	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	1	0.3	0.3
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Honduras, Central America	0	0.0	0.0
I don’t know	0	0.0	0.0
I was adopted, no info	0	0.0	0.0
Jamaica	1	0.3	0.3
JAMAICA	0	0.0	0.0
Jamaica WI	1	0.3	0.3
Jamaican	1	0.3	0.3
Macon County	0	0.0	0.0
Mississippi	1	0.3	0.3
Montserrat British VI	0	0.0	0.0
“NA”	302	95.9	95.9
Nassau Bahamas	0	0.0	0.0
Never knew my father	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Not known	1	0.3	0.3
Panama	1	0.3	0.3
Panama-Central America	0	0.0	0.0
Panama Canal Zone	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
United S Texas	1	0.3	0.3
Unknown	0	0.0	0.0
Venezuela	1	0.3	0.3
west Indies	0	0.0	0.0
Yatesville GA	0	0.0	0.0
Total	315	100.0	100.0

Detroit

a6: Where were you born?
	n	%	val%
US	345	96.9	98.3
Africa	1	0.3	0.3
Cuba_Caribbean	2	0.6	0.6
Other	3	0.8	0.9
Scantron_Error	0	0.0	0.0
NA	5	1.4	NA
Total	356	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Biological father unknown	0	0.0	0.0
Blackstone, VA	0	0.0	0.0
Bombay.	0	0.0	0.0
British Honduras.	0	0.0	0.0
Canada	1	0.3	0.3
Canada.	1	0.3	0.3
Central America (Panama)	0	0.0	0.0
Cleveland Mississippi	0	0.0	0.0
Dead	0	0.0	0.0
Don’t know	0	0.0	0.0
East Pakistan	1	0.3	0.3
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Father’s birthplace is unknown.	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Honduras, Central America	0	0.0	0.0
I don’t know	0	0.0	0.0
I was adopted, no info	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	352	98.9	98.9
Nassau Bahamas	0	0.0	0.0
Never knew my father	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Not known	0	0.0	0.0
Panama	0	0.0	0.0
Panama-Central America	0	0.0	0.0
Panama Canal Zone	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
United S Texas	0	0.0	0.0
Unknown	1	0.3	0.3
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Yatesville GA	0	0.0	0.0
Total	356	100.0	100.0

Louisiana

a6: Where were you born?
	n	%	val%
US	574	98.1	98.5
Africa	2	0.3	0.3
Cuba_Caribbean	5	0.9	0.9
Other	2	0.3	0.3
Scantron_Error	0	0.0	0.0
NA	2	0.3	NA
Total	585	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Biological father unknown	0	0.0	0.0
Blackstone, VA	0	0.0	0.0
Bombay.	0	0.0	0.0
British Honduras.	1	0.2	0.2
Canada	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Panama)	0	0.0	0.0
Cleveland Mississippi	0	0.0	0.0
Dead	0	0.0	0.0
Don’t know	0	0.0	0.0
East Pakistan	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Father’s birthplace is unknown.	0	0.0	0.0
Georgia Monroe Walton County	0	0.0	0.0
Germany	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Honduras, Central America	0	0.0	0.0
I don’t know	0	0.0	0.0
I was adopted, no info	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Macon County	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	583	99.7	99.7
Nassau Bahamas	0	0.0	0.0
Never knew my father	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
Not known	0	0.0	0.0
Panama	0	0.0	0.0
Panama-Central America	0	0.0	0.0
Panama Canal Zone	1	0.2	0.2
Panama City of Panama	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad.	0	0.0	0.0
United S Texas	0	0.0	0.0
Unknown	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Yatesville GA	0	0.0	0.0
Total	585	100.0	100.0

Georgia

a6: Where were you born?
	n	%	val%
US	1585	90.4	91.2
Africa	53	3.0	3.1
Cuba_Caribbean	66	3.8	3.8
Other	30	1.7	1.7
Scantron_Error	3	0.2	0.2
NA	17	1.0	NA
Total	1754	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	1	0.1	0.1
Belize	1	0.1	0.1
Belize Central America	1	0.1	0.1
Belize, Central America	0	0.0	0.0
Biological father unknown	1	0.1	0.1
Blackstone, VA	0	0.0	0.0
Bombay.	0	0.0	0.0
British Honduras.	0	0.0	0.0
Canada	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Panama)	1	0.1	0.1
Cleveland Mississippi	0	0.0	0.0
Dead	1	0.1	0.1
Don’t know	1	0.1	0.1
East Pakistan	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	1	0.1	0.1
Father’s birthplace is unknown.	1	0.1	0.1
Georgia Monroe Walton County	1	0.1	0.1
Germany	1	0.1	0.1
Ghana	1	0.1	0.1
Guyana	3	0.2	0.2
Guyana: South America	1	0.1	0.1
Haiti	4	0.2	0.2
Honduras, Central America	1	0.1	0.1
I don’t know	1	0.1	0.1
I was adopted, no info	1	0.1	0.1
Jamaica	7	0.4	0.4
JAMAICA	1	0.1	0.1
Jamaica WI	0	0.0	0.0
Jamaican	0	0.0	0.0
Macon County	1	0.1	0.1
Mississippi	1	0.1	0.1
Montserrat British VI	1	0.1	0.1
“NA”	1709	97.4	97.4
Nassau Bahamas	2	0.1	0.1
Never knew my father	1	0.1	0.1
Nigeria	2	0.1	0.1
Nigeria.	0	0.0	0.0
Not known	0	0.0	0.0
Panama	0	0.0	0.0
Panama-Central America	1	0.1	0.1
Panama Canal Zone	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	1	0.1	0.1
Trinidad and Tobago	1	0.1	0.1
Trinidad.	0	0.0	0.0
United S Texas	0	0.0	0.0
Unknown	1	0.1	0.1
Venezuela	0	0.0	0.0
west Indies	1	0.1	0.1
Yatesville GA	1	0.1	0.1
Total	1754	100.0	100.0

Michigan

a6: Where were you born?
	n	%	val%
US	15	93.8	93.8
Africa	0	0.0	0.0
Cuba_Caribbean	1	6.2	6.2
Other	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

a6 other: Where were you born?
	n	%	val%
Barbados	0	0	0
Belize	0	0	0
Belize Central America	0	0	0
Belize, Central America	0	0	0
Biological father unknown	0	0	0
Blackstone, VA	0	0	0
Bombay.	0	0	0
British Honduras.	0	0	0
Canada	0	0	0
Canada.	0	0	0
Central America (Panama)	0	0	0
Cleveland Mississippi	0	0	0
Dead	0	0	0
Don’t know	0	0	0
East Pakistan	0	0	0
England	0	0	0
Ethiopia	0	0	0
Father’s birthplace is unknown.	0	0	0
Georgia Monroe Walton County	0	0	0
Germany	0	0	0
Ghana	0	0	0
Guyana	0	0	0
Guyana: South America	0	0	0
Haiti	0	0	0
Honduras, Central America	0	0	0
I don’t know	0	0	0
I was adopted, no info	0	0	0
Jamaica	0	0	0
JAMAICA	0	0	0
Jamaica WI	0	0	0
Jamaican	0	0	0
Macon County	0	0	0
Mississippi	0	0	0
Montserrat British VI	0	0	0
“NA”	16	100	100
Nassau Bahamas	0	0	0
Never knew my father	0	0	0
Nigeria	0	0	0
Nigeria.	0	0	0
Not known	0	0	0
Panama	0	0	0
Panama-Central America	0	0	0
Panama Canal Zone	0	0	0
Panama City of Panama	0	0	0
Sierre Leone	0	0	0
Trinidad	0	0	0
Trinidad and Tobago	0	0	0
Trinidad.	0	0	0
United S Texas	0	0	0
Unknown	0	0	0
Venezuela	0	0	0
west Indies	0	0	0
Yatesville GA	0	0	0
Total	16	100	100

A7

A7. Where were you born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other

All data

a7 <- as.factor(d[,"a7"])
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4",
                   Scantron_Error="*")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")

a7: Where were you born?
	n	%	val%
US	3296	92.7	93.2
Africa	98	2.8	2.8
Cuba_Caribbean	96	2.7	2.7
Other	42	1.2	1.2
Scantron_Error	5	0.1	0.1
NA	20	0.6	NA
Total	3557	100.0	100.0

#a7: other
  a7other <- as.factor(d[,"a7other"])
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "Born place other")
  temp.d <- data.frame (new.d, a7other) 
  
  result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7 other: Where were you born?")

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	1	0.0	0.0
Barbados	1	0.0	0.0
Belize	1	0.0	0.0
Belize Central America	1	0.0	0.0
Belize, Central America	2	0.1	0.1
Bremont, TX	1	0.0	0.0
Buffalo NY	1	0.0	0.0
Canada.	1	0.0	0.0
Central America (Colon, Panama)	1	0.0	0.0
England	1	0.0	0.0
Ethiopia	1	0.0	0.0
Georgia Good Hope Walton County	1	0.0	0.0
Ghana	1	0.0	0.0
Guyana	4	0.1	0.1
Guyana: South America	1	0.0	0.0
Haiti	6	0.2	0.2
Jamaica	9	0.3	0.3
JAMAICA	1	0.0	0.0
Jamaica BWI Kingston	1	0.0	0.0
Jamaica WI	1	0.0	0.0
Japan	3	0.1	0.1
LA	1	0.0	0.0
Lamar County, GA	1	0.0	0.0
Macon County	1	0.0	0.0
Mexico	1	0.0	0.0
Mississippi	2	0.1	0.1
Montserrat British VI	1	0.0	0.0
“NA”	3490	98.1	98.1
Nassau Bahamas	1	0.0	0.0
Nigeria	2	0.1	0.1
Nigeria.	1	0.0	0.0
No biological info, I was adopted.	1	0.0	0.0
Oklahoma	1	0.0	0.0
Panama	3	0.1	0.1
Panama City of Panama	1	0.0	0.0
Puerto Rican	1	0.0	0.0
Sierre Leone	1	0.0	0.0
Trinidad	1	0.0	0.0
Trinidad and Tobago	1	0.0	0.0
Trinidad West Indies	1	0.0	0.0
Trinidad.	1	0.0	0.0
United States Texas	1	0.0	0.0
Venezuela	2	0.1	0.1
west Indies	1	0.0	0.0
Total	3557	100.0	100.0

LA County

a7: Where were you born?
	n	%	val%
US	291	90.7	91.2
Africa	10	3.1	3.1
Cuba_Caribbean	11	3.4	3.4
Other	5	1.6	1.6
Scantron_Error	2	0.6	0.6
NA	2	0.6	NA
Total	321	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Bremont, TX	0	0.0	0.0
Buffalo NY	1	0.3	0.3
Canada.	0	0.0	0.0
Central America (Colon, Panama)	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Good Hope Walton County	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	2	0.6	0.6
Jamaica	1	0.3	0.3
JAMAICA	0	0.0	0.0
Jamaica BWI Kingston	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Japan	0	0.0	0.0
LA	0	0.0	0.0
Lamar County, GA	0	0.0	0.0
Macon County	0	0.0	0.0
Mexico	1	0.3	0.3
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	311	96.9	96.9
Nassau Bahamas	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
No biological info, I was adopted.	0	0.0	0.0
Oklahoma	0	0.0	0.0
Panama	1	0.3	0.3
Panama City of Panama	1	0.3	0.3
Puerto Rican	0	0.0	0.0
Sierre Leone	1	0.3	0.3
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad West Indies	0	0.0	0.0
Trinidad.	1	0.3	0.3
United States Texas	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Total	321	100.0	100.0

Northern CA

a7: Where were you born?
	n	%	val%
US	192	91.4	91.4
Africa	12	5.7	5.7
Cuba_Caribbean	4	1.9	1.9
Other	2	1.0	1.0
Scantron_Error	0	0.0	0.0
Total	210	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	1	0.5	0.5
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Bremont, TX	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Colon, Panama)	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Good Hope Walton County	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Jamaica	1	0.5	0.5
JAMAICA	0	0.0	0.0
Jamaica BWI Kingston	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Japan	0	0.0	0.0
LA	0	0.0	0.0
Lamar County, GA	0	0.0	0.0
Macon County	0	0.0	0.0
Mexico	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	207	98.6	98.6
Nassau Bahamas	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	1	0.5	0.5
No biological info, I was adopted.	0	0.0	0.0
Oklahoma	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Puerto Rican	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad West Indies	0	0.0	0.0
Trinidad.	0	0.0	0.0
United States Texas	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Total	210	100.0	100.0

Greater CA

a7: Where were you born?
	n	%	val%
US	269	85.4	87.6
Africa	22	7.0	7.2
Cuba_Caribbean	6	1.9	2.0
Other	9	2.9	2.9
Scantron_Error	1	0.3	0.3
NA	8	2.5	NA
Total	315	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	1	0.3	0.3
Bremont, TX	1	0.3	0.3
Buffalo NY	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Colon, Panama)	0	0.0	0.0
England	1	0.3	0.3
Ethiopia	0	0.0	0.0
Georgia Good Hope Walton County	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	1	0.3	0.3
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica BWI Kingston	0	0.0	0.0
Jamaica WI	1	0.3	0.3
Japan	1	0.3	0.3
LA	0	0.0	0.0
Lamar County, GA	0	0.0	0.0
Macon County	0	0.0	0.0
Mexico	0	0.0	0.0
Mississippi	1	0.3	0.3
Montserrat British VI	0	0.0	0.0
“NA”	302	95.9	95.9
Nassau Bahamas	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
No biological info, I was adopted.	0	0.0	0.0
Oklahoma	1	0.3	0.3
Panama	1	0.3	0.3
Panama City of Panama	0	0.0	0.0
Puerto Rican	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad West Indies	1	0.3	0.3
Trinidad.	0	0.0	0.0
United States Texas	1	0.3	0.3
Venezuela	2	0.6	0.6
west Indies	0	0.0	0.0
Total	315	100.0	100.0

Detroit

a7: Where were you born?
	n	%	val%
US	349	98.0	98.6
Africa	1	0.3	0.3
Cuba_Caribbean	2	0.6	0.6
Other	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	356	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Bremont, TX	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Canada.	1	0.3	0.3
Central America (Colon, Panama)	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Good Hope Walton County	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica BWI Kingston	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Japan	1	0.3	0.3
LA	0	0.0	0.0
Lamar County, GA	0	0.0	0.0
Macon County	0	0.0	0.0
Mexico	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	354	99.4	99.4
Nassau Bahamas	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
No biological info, I was adopted.	0	0.0	0.0
Oklahoma	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Puerto Rican	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad West Indies	0	0.0	0.0
Trinidad.	0	0.0	0.0
United States Texas	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Total	356	100.0	100.0

Louisiana

a7: Where were you born?
	n	%	val%
US	576	98.5	98.8
Africa	1	0.2	0.2
Cuba_Caribbean	6	1.0	1.0
Other	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	2	0.3	NA
Total	585	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	0	0.0	0.0
Belize	0	0.0	0.0
Belize Central America	0	0.0	0.0
Belize, Central America	0	0.0	0.0
Bremont, TX	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Colon, Panama)	0	0.0	0.0
England	0	0.0	0.0
Ethiopia	0	0.0	0.0
Georgia Good Hope Walton County	0	0.0	0.0
Ghana	0	0.0	0.0
Guyana	0	0.0	0.0
Guyana: South America	0	0.0	0.0
Haiti	0	0.0	0.0
Jamaica	0	0.0	0.0
JAMAICA	0	0.0	0.0
Jamaica BWI Kingston	0	0.0	0.0
Jamaica WI	0	0.0	0.0
Japan	0	0.0	0.0
LA	1	0.2	0.2
Lamar County, GA	0	0.0	0.0
Macon County	0	0.0	0.0
Mexico	0	0.0	0.0
Mississippi	0	0.0	0.0
Montserrat British VI	0	0.0	0.0
“NA”	584	99.8	99.8
Nassau Bahamas	0	0.0	0.0
Nigeria	0	0.0	0.0
Nigeria.	0	0.0	0.0
No biological info, I was adopted.	0	0.0	0.0
Oklahoma	0	0.0	0.0
Panama	0	0.0	0.0
Panama City of Panama	0	0.0	0.0
Puerto Rican	0	0.0	0.0
Sierre Leone	0	0.0	0.0
Trinidad	0	0.0	0.0
Trinidad and Tobago	0	0.0	0.0
Trinidad West Indies	0	0.0	0.0
Trinidad.	0	0.0	0.0
United States Texas	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	0	0.0	0.0
Total	585	100.0	100.0

Georgia

a7: Where were you born?
	n	%	val%
US	1604	91.4	91.8
Africa	52	3.0	3.0
Cuba_Caribbean	66	3.8	3.8
Other	24	1.4	1.4
Scantron_Error	2	0.1	0.1
NA	6	0.3	NA
Total	1754	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0.0	0.0
Barbados	1	0.1	0.1
Belize	1	0.1	0.1
Belize Central America	1	0.1	0.1
Belize, Central America	0	0.0	0.0
Bremont, TX	0	0.0	0.0
Buffalo NY	0	0.0	0.0
Canada.	0	0.0	0.0
Central America (Colon, Panama)	1	0.1	0.1
England	0	0.0	0.0
Ethiopia	1	0.1	0.1
Georgia Good Hope Walton County	1	0.1	0.1
Ghana	1	0.1	0.1
Guyana	3	0.2	0.2
Guyana: South America	1	0.1	0.1
Haiti	4	0.2	0.2
Jamaica	7	0.4	0.4
JAMAICA	1	0.1	0.1
Jamaica BWI Kingston	1	0.1	0.1
Jamaica WI	0	0.0	0.0
Japan	1	0.1	0.1
LA	0	0.0	0.0
Lamar County, GA	1	0.1	0.1
Macon County	1	0.1	0.1
Mexico	0	0.0	0.0
Mississippi	1	0.1	0.1
Montserrat British VI	1	0.1	0.1
“NA”	1716	97.8	97.8
Nassau Bahamas	1	0.1	0.1
Nigeria	2	0.1	0.1
Nigeria.	0	0.0	0.0
No biological info, I was adopted.	1	0.1	0.1
Oklahoma	0	0.0	0.0
Panama	1	0.1	0.1
Panama City of Panama	0	0.0	0.0
Puerto Rican	1	0.1	0.1
Sierre Leone	0	0.0	0.0
Trinidad	1	0.1	0.1
Trinidad and Tobago	1	0.1	0.1
Trinidad West Indies	0	0.0	0.0
Trinidad.	0	0.0	0.0
United States Texas	0	0.0	0.0
Venezuela	0	0.0	0.0
west Indies	1	0.1	0.1
Total	1754	100.0	100.0

Michigan

a7: Where were you born?
	n	%	val%
US	15	93.8	93.8
Africa	0	0.0	0.0
Cuba_Caribbean	1	6.2	6.2
Other	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

a7 other: Where were you born?
	n	%	val%
Achtal, Bavaria, Germany	0	0	0
Barbados	0	0	0
Belize	0	0	0
Belize Central America	0	0	0
Belize, Central America	0	0	0
Bremont, TX	0	0	0
Buffalo NY	0	0	0
Canada.	0	0	0
Central America (Colon, Panama)	0	0	0
England	0	0	0
Ethiopia	0	0	0
Georgia Good Hope Walton County	0	0	0
Ghana	0	0	0
Guyana	0	0	0
Guyana: South America	0	0	0
Haiti	0	0	0
Jamaica	0	0	0
JAMAICA	0	0	0
Jamaica BWI Kingston	0	0	0
Jamaica WI	0	0	0
Japan	0	0	0
LA	0	0	0
Lamar County, GA	0	0	0
Macon County	0	0	0
Mexico	0	0	0
Mississippi	0	0	0
Montserrat British VI	0	0	0
“NA”	16	100	100
Nassau Bahamas	0	0	0
Nigeria	0	0	0
Nigeria.	0	0	0
No biological info, I was adopted.	0	0	0
Oklahoma	0	0	0
Panama	0	0	0
Panama City of Panama	0	0	0
Puerto Rican	0	0	0
Sierre Leone	0	0	0
Trinidad	0	0	0
Trinidad and Tobago	0	0	0
Trinidad West Indies	0	0	0
Trinidad.	0	0	0
United States Texas	0	0	0
Venezuela	0	0	0
west Indies	0	0	0
Total	16	100	100

A8

A8. How many years have you lived in the United States?
- 1=15 years or less
- 2=16-25 years
- 3=My whole life or more than 25 years

All data

a8 <- as.factor(d[,"a8"])
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3",
                   Scantron_Error="*")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life","Scantron_Error"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")

A8
	n	%	val%
less_or_15	25	0.7	0.7
years_16_25	52	1.5	1.5
more_than_25_or_whole_life	3400	95.6	97.8
Scantron_Error	1	0.0	0.0
NA	79	2.2	NA
Total	3557	100.0	100.0

LA County

A8
	n	%	val%
less_or_15	2	0.6	0.6
years_16_25	3	0.9	0.9
more_than_25_or_whole_life	315	98.1	98.4
Scantron_Error	0	0.0	0.0
NA	1	0.3	NA
Total	321	100.0	100.0

Northern CA

A8
	n	%	val%
less_or_15	4	1.9	1.9
years_16_25	4	1.9	1.9
more_than_25_or_whole_life	198	94.3	96.1
Scantron_Error	0	0.0	0.0
NA	4	1.9	NA
Total	210	100.0	100.0

Greater CA

A8
	n	%	val%
less_or_15	6	1.9	2.0
years_16_25	4	1.3	1.3
more_than_25_or_whole_life	297	94.3	96.7
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	315	100.0	100.0

Detroit

A8
	n	%	val%
less_or_15	0	0.0	0.0
years_16_25	2	0.6	0.6
more_than_25_or_whole_life	342	96.1	99.1
Scantron_Error	1	0.3	0.3
NA	11	3.1	NA
Total	356	100.0	100.0

Louisiana

A8
	n	%	val%
less_or_15	0	0.0	0.0
years_16_25	4	0.7	0.7
more_than_25_or_whole_life	571	97.6	99.3
Scantron_Error	0	0.0	0.0
NA	10	1.7	NA
Total	585	100.0	100.0

Georgia

A8
	n	%	val%
less_or_15	13	0.7	0.8
years_16_25	35	2.0	2.0
more_than_25_or_whole_life	1661	94.7	97.2
Scantron_Error	0	0.0	0.0
NA	45	2.6	NA
Total	1754	100.0	100.0

Michigan

A8
	n	%	val%
less_or_15	0	0	0
years_16_25	0	0	0
more_than_25_or_whole_life	16	100	100
Scantron_Error	0	0	0
Total	16	100	100

B1A: Father

B1Aa: Father: Has this person had prostate cancer?
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
B1Ac: Father: Did he (or any) die of prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know

All data

# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	1979	55.6	58.2
Yes	649	18.2	19.1
Dont_know	772	21.7	22.7
Scantron_Error	0	0.0	0.0
NA	157	4.4	NA
Total	3557	100.0	100.0

#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	692	19.5	58.4
Yes	82	2.3	6.9
Dont_know	408	11.5	34.5
Scantron_Error	2	0.1	0.2
NA	2373	66.7	NA
Total	3557	100.0	100.0

#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	755	21.2	62.8
Yes	224	6.3	18.6
Dont_know	223	6.3	18.5
Scantron_Error	1	0.0	0.1
NA	2354	66.2	NA
Total	3557	100.0	100.0

LA County

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	203	63.2	64.2
Yes	51	15.9	16.1
Dont_know	62	19.3	19.6
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	54	16.8	56.2
Yes	5	1.6	5.2
Dont_know	37	11.5	38.5
Scantron_Error	0	0.0	0.0
NA	225	70.1	NA
Total	321	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	61	19.0	64.2
Yes	15	4.7	15.8
Dont_know	18	5.6	18.9
Scantron_Error	1	0.3	1.1
NA	226	70.4	NA
Total	321	100.0	100.0

Northern CA

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	112	53.3	56.3
Yes	48	22.9	24.1
Dont_know	39	18.6	19.6
Scantron_Error	0	0.0	0.0
NA	11	5.2	NA
Total	210	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	43	20.5	65.2
Yes	5	2.4	7.6
Dont_know	18	8.6	27.3
Scantron_Error	0	0.0	0.0
NA	144	68.6	NA
Total	210	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	39	18.6	59.1
Yes	14	6.7	21.2
Dont_know	13	6.2	19.7
Scantron_Error	0	0.0	0.0
NA	144	68.6	NA
Total	210	100.0	100.0

Greater CA

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	159	50.5	52.8
Yes	64	20.3	21.3
Dont_know	78	24.8	25.9
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	67	21.3	62.6
Yes	6	1.9	5.6
Dont_know	34	10.8	31.8
Scantron_Error	0	0.0	0.0
NA	208	66.0	NA
Total	315	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	71	22.5	64.5
Yes	28	8.9	25.5
Dont_know	11	3.5	10.0
Scantron_Error	0	0.0	0.0
NA	205	65.1	NA
Total	315	100.0	100.0

Detroit

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	204	57.3	59.8
Yes	64	18.0	18.8
Dont_know	73	20.5	21.4
Scantron_Error	0	0.0	0.0
NA	15	4.2	NA
Total	356	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	68	19.1	57.6
Yes	9	2.5	7.6
Dont_know	41	11.5	34.7
Scantron_Error	0	0.0	0.0
NA	238	66.9	NA
Total	356	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	81	22.8	68.1
Yes	21	5.9	17.6
Dont_know	17	4.8	14.3
Scantron_Error	0	0.0	0.0
NA	237	66.6	NA
Total	356	100.0	100.0

Louisiana

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	338	57.8	60.6
Yes	106	18.1	19.0
Dont_know	114	19.5	20.4
Scantron_Error	0	0.0	0.0
NA	27	4.6	NA
Total	585	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	117	20.0	57.4
Yes	14	2.4	6.9
Dont_know	72	12.3	35.3
Scantron_Error	1	0.2	0.5
NA	381	65.1	NA
Total	585	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	130	22.2	62.5
Yes	37	6.3	17.8
Dont_know	41	7.0	19.7
Scantron_Error	0	0.0	0.0
NA	377	64.4	NA
Total	585	100.0	100.0

Georgia

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	957	54.6	57.3
Yes	309	17.6	18.5
Dont_know	403	23.0	24.1
Scantron_Error	0	0.0	0.0
NA	85	4.8	NA
Total	1754	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	336	19.2	57.6
Yes	41	2.3	7.0
Dont_know	205	11.7	35.2
Scantron_Error	1	0.1	0.2
NA	1171	66.8	NA
Total	1754	100.0	100.0

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	366	20.9	61.5
Yes	106	6.0	17.8
Dont_know	123	7.0	20.7
Scantron_Error	0	0.0	0.0
NA	1159	66.1	NA
Total	1754	100.0	100.0

Michigan

B1Aa: Father: Has this person had prostate cancer?
	n	%	val%
No	6	37.5	37.5
Yes	7	43.8	43.8
Dont_know	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	7	43.8	70
Yes	2	12.5	20
Dont_know	1	6.2	10
Scantron_Error	0	0.0	0
NA	6	37.5	NA
Total	16	100.0	100

B1Ac: Father: Did he (or any) die of prostate cancer?
	n	%	val%
No	7	43.8	70
Yes	3	18.8	30
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	6	37.5	NA
Total	16	100.0	100

B1B: Any Brother

B1BNo: Any Brother
- 1=I had no brothers
- if not marked
B1Ba: Any Brother: Has this person had prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know
B1Ba2: Any Brother: If Yes, number with prostate cancer
- 1=1
- 2=2+
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
- 1=No
- 2=Yes
- 88=Don’t know
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know

All data

# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1",
                        Scantron_Error="*")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")

B1BNo: Any Brother
	n	%	val%
No_brothers	321	9	100
Scantron_Error	0	0	0
NA	3236	91	NA
Total	3557	100	100

#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	2110	59.3	66.8
Yes	708	19.9	22.4
Dont_know	338	9.5	10.7
Scantron_Error	2	0.1	0.1
NA	399	11.2	NA
Total	3557	100.0	100.0

#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2",
                     Scantron_Error="*")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	288	8.1	63.2
Two_or_more	167	4.7	36.6
Scantron_Error	1	0.0	0.2
NA	3101	87.2	NA
Total	3557	100.0	100.0

#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	685	19.3	62.8
Yes	167	4.7	15.3
Dont_know	239	6.7	21.9
Scantron_Error	0	0.0	0.0
NA	2466	69.3	NA
Total	3557	100.0	100.0

#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	873	24.5	81.4
Yes	84	2.4	7.8
Dont_know	115	3.2	10.7
Scantron_Error	0	0.0	0.0
NA	2485	69.9	NA
Total	3557	100.0	100.0

LA County

B1BNo: Any Brother
	n	%	val%
No_brothers	29	9	100
Scantron_Error	0	0	0
NA	292	91	NA
Total	321	100	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	201	62.6	67.7
Yes	70	21.8	23.6
Dont_know	26	8.1	8.8
Scantron_Error	0	0.0	0.0
NA	24	7.5	NA
Total	321	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	26	8.1	65
Two_or_more	14	4.4	35
Scantron_Error	0	0.0	0
NA	281	87.5	NA
Total	321	100.0	100

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	59	18.4	62.1
Yes	18	5.6	18.9
Dont_know	18	5.6	18.9
Scantron_Error	0	0.0	0.0
NA	226	70.4	NA
Total	321	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	80	24.9	84.2
Yes	7	2.2	7.4
Dont_know	8	2.5	8.4
Scantron_Error	0	0.0	0.0
NA	226	70.4	NA
Total	321	100.0	100.0

Northern CA

B1BNo: Any Brother
	n	%	val%
No_brothers	32	15.2	100
Scantron_Error	0	0.0	0
NA	178	84.8	NA
Total	210	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	120	57.1	69.0
Yes	28	13.3	16.1
Dont_know	26	12.4	14.9
Scantron_Error	0	0.0	0.0
NA	36	17.1	NA
Total	210	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	19	9.0	79.2
Two_or_more	4	1.9	16.7
Scantron_Error	1	0.5	4.2
NA	186	88.6	NA
Total	210	100.0	100.0

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	28	13.3	57.1
Yes	7	3.3	14.3
Dont_know	14	6.7	28.6
Scantron_Error	0	0.0	0.0
NA	161	76.7	NA
Total	210	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	35	16.7	76.1
Yes	1	0.5	2.2
Dont_know	10	4.8	21.7
Scantron_Error	0	0.0	0.0
NA	164	78.1	NA
Total	210	100.0	100.0

Greater CA

B1BNo: Any Brother
	n	%	val%
No_brothers	28	8.9	100
Scantron_Error	0	0.0	0
NA	287	91.1	NA
Total	315	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	177	56.2	63.9
Yes	72	22.9	26.0
Dont_know	28	8.9	10.1
Scantron_Error	0	0.0	0.0
NA	38	12.1	NA
Total	315	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	30	9.5	65.2
Two_or_more	16	5.1	34.8
Scantron_Error	0	0.0	0.0
NA	269	85.4	NA
Total	315	100.0	100.0

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	63	20.0	60.0
Yes	24	7.6	22.9
Dont_know	18	5.7	17.1
Scantron_Error	0	0.0	0.0
NA	210	66.7	NA
Total	315	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	81	25.7	81
Yes	13	4.1	13
Dont_know	6	1.9	6
Scantron_Error	0	0.0	0
NA	215	68.3	NA
Total	315	100.0	100

Detroit

B1BNo: Any Brother
	n	%	val%
No_brothers	35	9.8	100
Scantron_Error	0	0.0	0
NA	321	90.2	NA
Total	356	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	216	60.7	69.2
Yes	64	18.0	20.5
Dont_know	32	9.0	10.3
Scantron_Error	0	0.0	0.0
NA	44	12.4	NA
Total	356	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	26	7.3	61.9
Two_or_more	16	4.5	38.1
Scantron_Error	0	0.0	0.0
NA	314	88.2	NA
Total	356	100.0	100.0

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	63	17.7	58.9
Yes	14	3.9	13.1
Dont_know	30	8.4	28.0
Scantron_Error	0	0.0	0.0
NA	249	69.9	NA
Total	356	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	83	23.3	79.8
Yes	10	2.8	9.6
Dont_know	11	3.1	10.6
Scantron_Error	0	0.0	0.0
NA	252	70.8	NA
Total	356	100.0	100.0

Louisiana

B1BNo: Any Brother
	n	%	val%
No_brothers	31	5.3	100
Scantron_Error	0	0.0	0
NA	554	94.7	NA
Total	585	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	373	63.8	68.7
Yes	114	19.5	21.0
Dont_know	56	9.6	10.3
Scantron_Error	0	0.0	0.0
NA	42	7.2	NA
Total	585	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	42	7.2	60.9
Two_or_more	27	4.6	39.1
Scantron_Error	0	0.0	0.0
NA	516	88.2	NA
Total	585	100.0	100.0

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	138	23.6	69.3
Yes	26	4.4	13.1
Dont_know	35	6.0	17.6
Scantron_Error	0	0.0	0.0
NA	386	66.0	NA
Total	585	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	161	27.5	84.3
Yes	10	1.7	5.2
Dont_know	20	3.4	10.5
Scantron_Error	0	0.0	0.0
NA	394	67.4	NA
Total	585	100.0	100.0

Georgia

B1BNo: Any Brother
	n	%	val%
No_brothers	163	9.3	100
Scantron_Error	0	0.0	0
NA	1591	90.7	NA
Total	1754	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	1015	57.9	65.8
Yes	356	20.3	23.1
Dont_know	169	9.6	11.0
Scantron_Error	2	0.1	0.1
NA	212	12.1	NA
Total	1754	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	144	8.2	61.5
Two_or_more	90	5.1	38.5
Scantron_Error	0	0.0	0.0
NA	1520	86.7	NA
Total	1754	100.0	100.0

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	331	18.9	62.3
Yes	76	4.3	14.3
Dont_know	124	7.1	23.4
Scantron_Error	0	0.0	0.0
NA	1223	69.7	NA
Total	1754	100.0	100.0

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	428	24.4	80.8
Yes	43	2.5	8.1
Dont_know	59	3.4	11.1
Scantron_Error	0	0.0	0.0
NA	1224	69.8	NA
Total	1754	100.0	100.0

Michigan

B1BNo: Any Brother
	n	%	val%
No_brothers	3	18.8	100
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

B1Ba: Any Brother: Has this person had prostate cancer?
	n	%	val%
No	8	50.0	61.5
Yes	4	25.0	30.8
Dont_know	1	6.2	7.7
Scantron_Error	0	0.0	0.0
NA	3	18.8	NA
Total	16	100.0	100.0

B1Ba2: Any Brother: If Yes, number with prostate cancer
	n	%	val%
One	1	6.2	100
Two_or_more	0	0.0	0
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	3	18.8	60
Yes	2	12.5	40
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

B1Bc: Any Brother: Did he (or any) die of prostate cancer?
	n	%	val%
No	5	31.2	83.3
Yes	0	0.0	0.0
Dont_know	1	6.2	16.7
Scantron_Error	0	0.0	0.0
NA	10	62.5	NA
Total	16	100.0	100.0

B1C: Any Son

B1CNo: Any Son
- 1=I had no sons
- if not marked
B1Ca: Any Son: Has this person had prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know
B1Ca2: Any Son: If Yes, number with prostate cancer
- 1=1
- 2=2+
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
- 1=No
- 2=Yes
- 88=Don’t know
B1Cc: Any Son: Did he (or any) die of prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know

All data

# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1",
                        Scantron_Error="*")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")

B1CNo: Any Son
	n	%	val%
No_brothers	608	17.1	100
Scantron_Error	0	0.0	0
NA	2949	82.9	NA
Total	3557	100.0	100

#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	2621	73.7	93.2
Yes	87	2.4	3.1
Dont_know	104	2.9	3.7
Scantron_Error	1	0.0	0.0
NA	744	20.9	NA
Total	3557	100.0	100.0

#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2",
                     Scantron_Error="*")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	33	0.9	48.5
Two_or_more	34	1.0	50.0
Scantron_Error	1	0.0	1.5
NA	3489	98.1	NA
Total	3557	100.0	100.0

#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	508	14.3	85.1
Yes	12	0.3	2.0
Dont_know	77	2.2	12.9
Scantron_Error	0	0.0	0.0
NA	2960	83.2	NA
Total	3557	100.0	100.0

#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	518	14.6	88.5
Yes	3	0.1	0.5
Dont_know	64	1.8	10.9
Scantron_Error	0	0.0	0.0
NA	2972	83.6	NA
Total	3557	100.0	100.0

LA County

B1CNo: Any Son
	n	%	val%
No_brothers	46	14.3	100
Scantron_Error	0	0.0	0
NA	275	85.7	NA
Total	321	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	260	81.0	96.3
Yes	3	0.9	1.1
Dont_know	7	2.2	2.6
Scantron_Error	0	0.0	0.0
NA	51	15.9	NA
Total	321	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	3	0.9	42.9
Two_or_more	4	1.2	57.1
Scantron_Error	0	0.0	0.0
NA	314	97.8	NA
Total	321	100.0	100.0

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	34	10.6	77.3
Yes	2	0.6	4.5
Dont_know	8	2.5	18.2
Scantron_Error	0	0.0	0.0
NA	277	86.3	NA
Total	321	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	33	10.3	84.6
Yes	0	0.0	0.0
Dont_know	6	1.9	15.4
Scantron_Error	0	0.0	0.0
NA	282	87.9	NA
Total	321	100.0	100.0

Northern CA

B1CNo: Any Son
	n	%	val%
No_brothers	47	22.4	100
Scantron_Error	0	0.0	0
NA	163	77.6	NA
Total	210	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	145	69.0	95.4
Yes	4	1.9	2.6
Dont_know	3	1.4	2.0
Scantron_Error	0	0.0	0.0
NA	58	27.6	NA
Total	210	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	3	1.4	42.9
Two_or_more	4	1.9	57.1
Scantron_Error	0	0.0	0.0
NA	203	96.7	NA
Total	210	100.0	100.0

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	19	9.0	79.2
Yes	1	0.5	4.2
Dont_know	4	1.9	16.7
Scantron_Error	0	0.0	0.0
NA	186	88.6	NA
Total	210	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	20	9.5	87.0
Yes	1	0.5	4.3
Dont_know	2	1.0	8.7
Scantron_Error	0	0.0	0.0
NA	187	89.0	NA
Total	210	100.0	100.0

Greater CA

B1CNo: Any Son
	n	%	val%
No_brothers	53	16.8	100
Scantron_Error	0	0.0	0
NA	262	83.2	NA
Total	315	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	237	75.2	92.2
Yes	10	3.2	3.9
Dont_know	10	3.2	3.9
Scantron_Error	0	0.0	0.0
NA	58	18.4	NA
Total	315	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	2	0.6	50
Two_or_more	2	0.6	50
Scantron_Error	0	0.0	0
NA	311	98.7	NA
Total	315	100.0	100

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	50	15.9	92.6
Yes	0	0.0	0.0
Dont_know	4	1.3	7.4
Scantron_Error	0	0.0	0.0
NA	261	82.9	NA
Total	315	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	49	15.6	94.2
Yes	0	0.0	0.0
Dont_know	3	1.0	5.8
Scantron_Error	0	0.0	0.0
NA	263	83.5	NA
Total	315	100.0	100.0

Detroit

B1CNo: Any Son
	n	%	val%
No_brothers	71	19.9	100
Scantron_Error	0	0.0	0
NA	285	80.1	NA
Total	356	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	254	71.3	94.1
Yes	8	2.2	3.0
Dont_know	8	2.2	3.0
Scantron_Error	0	0.0	0.0
NA	86	24.2	NA
Total	356	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	3	0.8	75
Two_or_more	1	0.3	25
Scantron_Error	0	0.0	0
NA	352	98.9	NA
Total	356	100.0	100

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	48	13.5	87.3
Yes	1	0.3	1.8
Dont_know	6	1.7	10.9
Scantron_Error	0	0.0	0.0
NA	301	84.6	NA
Total	356	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	49	13.8	89.1
Yes	1	0.3	1.8
Dont_know	5	1.4	9.1
Scantron_Error	0	0.0	0.0
NA	301	84.6	NA
Total	356	100.0	100.0

Louisiana

B1CNo: Any Son
	n	%	val%
No_brothers	82	14	100
Scantron_Error	0	0	0
NA	503	86	NA
Total	585	100	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	443	75.7	93.1
Yes	15	2.6	3.2
Dont_know	18	3.1	3.8
Scantron_Error	0	0.0	0.0
NA	109	18.6	NA
Total	585	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	4	0.7	40
Two_or_more	5	0.9	50
Scantron_Error	1	0.2	10
NA	575	98.3	NA
Total	585	100.0	100

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	95	16.2	84.8
Yes	1	0.2	0.9
Dont_know	16	2.7	14.3
Scantron_Error	0	0.0	0.0
NA	473	80.9	NA
Total	585	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	94	16.1	86.2
Yes	1	0.2	0.9
Dont_know	14	2.4	12.8
Scantron_Error	0	0.0	0.0
NA	476	81.4	NA
Total	585	100.0	100.0

Georgia

B1CNo: Any Son
	n	%	val%
No_brothers	306	17.4	100
Scantron_Error	0	0.0	0
NA	1448	82.6	NA
Total	1754	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	1270	72.4	92.3
Yes	47	2.7	3.4
Dont_know	58	3.3	4.2
Scantron_Error	1	0.1	0.1
NA	378	21.6	NA
Total	1754	100.0	100.0

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	18	1.0	50
Two_or_more	18	1.0	50
Scantron_Error	0	0.0	0
NA	1718	97.9	NA
Total	1754	100.0	100

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	259	14.8	84.9
Yes	7	0.4	2.3
Dont_know	39	2.2	12.8
Scantron_Error	0	0.0	0.0
NA	1449	82.6	NA
Total	1754	100.0	100.0

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	270	15.4	88.8
Yes	0	0.0	0.0
Dont_know	34	1.9	11.2
Scantron_Error	0	0.0	0.0
NA	1450	82.7	NA
Total	1754	100.0	100.0

Michigan

B1CNo: Any Son
	n	%	val%
No_brothers	3	18.8	100
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

B1Ca: Any Son: Has this person had prostate cancer?
	n	%	val%
No	12	75	100
Yes	0	0	0
Dont_know	0	0	0
Scantron_Error	0	0	0
NA	4	25	NA
Total	16	100	100

B1Ca2: Any Son: If Yes, number with prostate cancer
	n	%	val%
One	0	0	NaN
Two_or_more	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	3	18.8	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

B1Cc: Any Son: Did he (or any) die of prostate cancer?
	n	%	val%
No	3	18.8	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

B1D: Maternal Grandfather

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know

All data

# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1da <- ordered(b1da, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	1474	41.4	44.6
Yes	104	2.9	3.1
Dont_know	1726	48.5	52.2
Scantron_Error	4	0.1	0.1
NA	249	7.0	NA
Total	3557	100.0	100.0

# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1db <- ordered(b1db, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	286	8.0	38.2
Yes	10	0.3	1.3
Dont_know	452	12.7	60.4
Scantron_Error	0	0.0	0.0
NA	2809	79.0	NA
Total	3557	100.0	100.0

# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	292	8.2	38.5
Yes	42	1.2	5.5
Dont_know	425	11.9	56.0
Scantron_Error	0	0.0	0.0
NA	2798	78.7	NA
Total	3557	100.0	100.0

LA County

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	161	50.2	51.8
Yes	6	1.9	1.9
Dont_know	143	44.5	46.0
Scantron_Error	1	0.3	0.3
NA	10	3.1	NA
Total	321	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	17	5.3	29.8
Yes	0	0.0	0.0
Dont_know	40	12.5	70.2
Scantron_Error	0	0.0	0.0
NA	264	82.2	NA
Total	321	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	17	5.3	28.8
Yes	3	0.9	5.1
Dont_know	39	12.1	66.1
Scantron_Error	0	0.0	0.0
NA	262	81.6	NA
Total	321	100.0	100.0

Northern CA

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	67	31.9	34.9
Yes	6	2.9	3.1
Dont_know	119	56.7	62.0
Scantron_Error	0	0.0	0.0
NA	18	8.6	NA
Total	210	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	12	5.7	36.4
Yes	1	0.5	3.0
Dont_know	20	9.5	60.6
Scantron_Error	0	0.0	0.0
NA	177	84.3	NA
Total	210	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	13	6.2	39.4
Yes	2	1.0	6.1
Dont_know	18	8.6	54.5
Scantron_Error	0	0.0	0.0
NA	177	84.3	NA
Total	210	100.0	100.0

Greater CA

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	127	40.3	42.5
Yes	10	3.2	3.3
Dont_know	162	51.4	54.2
Scantron_Error	0	0.0	0.0
NA	16	5.1	NA
Total	315	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	30	9.5	42.3
Yes	1	0.3	1.4
Dont_know	40	12.7	56.3
Scantron_Error	0	0.0	0.0
NA	244	77.5	NA
Total	315	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	31	9.8	44.9
Yes	6	1.9	8.7
Dont_know	32	10.2	46.4
Scantron_Error	0	0.0	0.0
NA	246	78.1	NA
Total	315	100.0	100.0

Detroit

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	151	42.4	45.2
Yes	16	4.5	4.8
Dont_know	166	46.6	49.7
Scantron_Error	1	0.3	0.3
NA	22	6.2	NA
Total	356	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	28	7.9	40.6
Yes	2	0.6	2.9
Dont_know	39	11.0	56.5
Scantron_Error	0	0.0	0.0
NA	287	80.6	NA
Total	356	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	29	8.1	42.0
Yes	4	1.1	5.8
Dont_know	36	10.1	52.2
Scantron_Error	0	0.0	0.0
NA	287	80.6	NA
Total	356	100.0	100.0

Louisiana

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	261	44.6	49.1
Yes	21	3.6	3.9
Dont_know	250	42.7	47.0
Scantron_Error	0	0.0	0.0
NA	53	9.1	NA
Total	585	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	58	9.9	40.0
Yes	3	0.5	2.1
Dont_know	84	14.4	57.9
Scantron_Error	0	0.0	0.0
NA	440	75.2	NA
Total	585	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	56	9.6	39.2
Yes	9	1.5	6.3
Dont_know	78	13.3	54.5
Scantron_Error	0	0.0	0.0
NA	442	75.6	NA
Total	585	100.0	100.0

Georgia

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	703	40.1	43.3
Yes	44	2.5	2.7
Dont_know	876	49.9	53.9
Scantron_Error	2	0.1	0.1
NA	129	7.4	NA
Total	1754	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	140	8.0	38.0
Yes	2	0.1	0.5
Dont_know	226	12.9	61.4
Scantron_Error	0	0.0	0.0
NA	1386	79.0	NA
Total	1754	100.0	100.0

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	145	8.3	38.1
Yes	18	1.0	4.7
Dont_know	218	12.4	57.2
Scantron_Error	0	0.0	0.0
NA	1373	78.3	NA
Total	1754	100.0	100.0

Michigan

B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
	n	%	val%
No	4	25.0	26.7
Yes	1	6.2	6.7
Dont_know	10	62.5	66.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	1	6.2	20
Yes	1	6.2	20
Dont_know	3	18.8	60
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	1	6.2	20
Yes	0	0.0	0
Dont_know	4	25.0	80
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

B1E: Paternal Grandfather

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
- 1=No
- 2=Yes
- 88=Don’t know

All data

# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	1327	37.3	40.5
Yes	97	2.7	3.0
Dont_know	1851	52.0	56.5
Scantron_Error	1	0.0	0.0
NA	281	7.9	NA
Total	3557	100.0	100.0

# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	241	6.8	33.1
Yes	15	0.4	2.1
Dont_know	472	13.3	64.8
Scantron_Error	0	0.0	0.0
NA	2829	79.5	NA
Total	3557	100.0	100.0

# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	256	7.2	34.2
Yes	47	1.3	6.3
Dont_know	446	12.5	59.5
Scantron_Error	0	0.0	0.0
NA	2808	78.9	NA
Total	3557	100.0	100.0

LA County

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	146	45.5	47.7
Yes	9	2.8	2.9
Dont_know	151	47.0	49.3
Scantron_Error	0	0.0	0.0
NA	15	4.7	NA
Total	321	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	18	5.6	31.6
Yes	0	0.0	0.0
Dont_know	39	12.1	68.4
Scantron_Error	0	0.0	0.0
NA	264	82.2	NA
Total	321	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	20	6.2	33.9
Yes	2	0.6	3.4
Dont_know	37	11.5	62.7
Scantron_Error	0	0.0	0.0
NA	262	81.6	NA
Total	321	100.0	100.0

Northern CA

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	55	26.2	28.6
Yes	11	5.2	5.7
Dont_know	126	60.0	65.6
Scantron_Error	0	0.0	0.0
NA	18	8.6	NA
Total	210	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	12	5.7	28.6
Yes	3	1.4	7.1
Dont_know	27	12.9	64.3
Scantron_Error	0	0.0	0.0
NA	168	80.0	NA
Total	210	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	13	6.2	31.7
Yes	6	2.9	14.6
Dont_know	22	10.5	53.7
Scantron_Error	0	0.0	0.0
NA	169	80.5	NA
Total	210	100.0	100.0

Greater CA

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	109	34.6	36.7
Yes	7	2.2	2.4
Dont_know	181	57.5	60.9
Scantron_Error	0	0.0	0.0
NA	18	5.7	NA
Total	315	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	25	7.9	38.5
Yes	0	0.0	0.0
Dont_know	40	12.7	61.5
Scantron_Error	0	0.0	0.0
NA	250	79.4	NA
Total	315	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	24	7.6	36.4
Yes	2	0.6	3.0
Dont_know	40	12.7	60.6
Scantron_Error	0	0.0	0.0
NA	249	79.0	NA
Total	315	100.0	100.0

Detroit

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	148	41.6	45.0
Yes	10	2.8	3.0
Dont_know	170	47.8	51.7
Scantron_Error	1	0.3	0.3
NA	27	7.6	NA
Total	356	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	26	7.3	41.9
Yes	1	0.3	1.6
Dont_know	35	9.8	56.5
Scantron_Error	0	0.0	0.0
NA	294	82.6	NA
Total	356	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	25	7.0	39.1
Yes	6	1.7	9.4
Dont_know	33	9.3	51.6
Scantron_Error	0	0.0	0.0
NA	292	82.0	NA
Total	356	100.0	100.0

Louisiana

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	238	40.7	45.2
Yes	21	3.6	4.0
Dont_know	267	45.6	50.8
Scantron_Error	0	0.0	0.0
NA	59	10.1	NA
Total	585	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	46	7.9	32.6
Yes	5	0.9	3.5
Dont_know	90	15.4	63.8
Scantron_Error	0	0.0	0.0
NA	444	75.9	NA
Total	585	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	48	8.2	34.5
Yes	11	1.9	7.9
Dont_know	80	13.7	57.6
Scantron_Error	0	0.0	0.0
NA	446	76.2	NA
Total	585	100.0	100.0

Georgia

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	627	35.7	38.9
Yes	38	2.2	2.4
Dont_know	946	53.9	58.7
Scantron_Error	0	0.0	0.0
NA	143	8.2	NA
Total	1754	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	113	6.4	31.7
Yes	6	0.3	1.7
Dont_know	238	13.6	66.7
Scantron_Error	0	0.0	0.0
NA	1397	79.6	NA
Total	1754	100.0	100.0

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	125	7.1	33.2
Yes	19	1.1	5.1
Dont_know	232	13.2	61.7
Scantron_Error	0	0.0	0.0
NA	1378	78.6	NA
Total	1754	100.0	100.0

Michigan

B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
	n	%	val%
No	4	25.0	26.7
Yes	1	6.2	6.7
Dont_know	10	62.5	66.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
	n	%	val%
No	1	6.2	25
Yes	0	0.0	0
Dont_know	3	18.8	75
Scantron_Error	0	0.0	0
NA	12	75.0	NA
Total	16	100.0	100

B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
	n	%	val%
No	1	6.2	25
Yes	1	6.2	25
Dont_know	2	12.5	50
Scantron_Error	0	0.0	0
NA	12	75.0	NA
Total	16	100.0	100

B3

B3. In general, how would you rate your current health?
- 1=Excellent
- 2=Very Good
- 3=Good
- 4=Fair
- 5=Poor

All data

  b3 <- as.factor(d[,"b3"])
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5",
                     Scantron_Error="*")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor","Scantron_Error"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')

	n	%	val%	%cum	val%cum
Excellent	221	6.2	6.5	6.2	6.5
Very_Good	933	26.2	27.3	32.4	33.8
Good	1439	40.5	42.1	72.9	75.9
Fair	724	20.4	21.2	93.3	97.1
Poor	95	2.7	2.8	95.9	99.9
Scantron_Error	3	0.1	0.1	96.0	100.0
NA	142	4.0	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

	n	%	val%	%cum	val%cum
Excellent	27	8.4	8.7	8.4	8.7
Very_Good	102	31.8	32.7	40.2	41.3
Good	120	37.4	38.5	77.6	79.8
Fair	51	15.9	16.3	93.5	96.2
Poor	12	3.7	3.8	97.2	100.0
Scantron_Error	0	0.0	0.0	97.2	100.0
NA	9	2.8	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

	n	%	val%	%cum	val%cum
Excellent	14	6.7	7.0	6.7	7.0
Very_Good	61	29.0	30.7	35.7	37.7
Good	81	38.6	40.7	74.3	78.4
Fair	41	19.5	20.6	93.8	99.0
Poor	1	0.5	0.5	94.3	99.5
Scantron_Error	1	0.5	0.5	94.8	100.0
NA	11	5.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

	n	%	val%	%cum	val%cum
Excellent	21	6.7	7.0	6.7	7.0
Very_Good	92	29.2	30.5	35.9	37.4
Good	122	38.7	40.4	74.6	77.8
Fair	58	18.4	19.2	93.0	97.0
Poor	9	2.9	3.0	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

	n	%	val%	%cum	val%cum
Excellent	20	5.6	5.8	5.6	5.8
Very_Good	72	20.2	21.1	25.8	26.9
Good	145	40.7	42.4	66.6	69.3
Fair	87	24.4	25.4	91.0	94.7
Poor	17	4.8	5.0	95.8	99.7
Scantron_Error	1	0.3	0.3	96.1	100.0
NA	14	3.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

	n	%	val%	%cum	val%cum
Excellent	29	5.0	5.2	5.0	5.2
Very_Good	135	23.1	24.0	28.0	29.1
Good	236	40.3	41.9	68.4	71.0
Fair	144	24.6	25.6	93.0	96.6
Poor	19	3.2	3.4	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	22	3.8	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

	n	%	val%	%cum	val%cum
Excellent	109	6.2	6.5	6.2	6.5
Very_Good	466	26.6	27.7	32.8	34.2
Good	728	41.5	43.3	74.3	77.5
Fair	341	19.4	20.3	93.7	97.8
Poor	36	2.1	2.1	95.8	99.9
Scantron_Error	1	0.1	0.1	95.8	100.0
NA	73	4.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

	n	%	val%	%cum	val%cum
Excellent	1	6.2	6.2	6.2	6.2
Very_Good	5	31.2	31.2	37.5	37.5
Good	7	43.8	43.8	81.2	81.2
Fair	2	12.5	12.5	93.8	93.8
Poor	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

B4

B4. Has the doctor ever told you that you have/had…
- Heart Attack
- Heart Failure or CHF
- Stroke
- Hypertension
- Peripheral arterial disease
- High Cholesterol
- Asthma, COPD
- Stomach ulcers
- Crohn’s Disease
- Diabetes
- Kidney Problems
- Cirrhosis, liver damage
- Arthritis
- Dementia
- Depression
- AIDS
- Other Cancer

All data

# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
  levels(b4aa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4aa <- ordered(b4aa, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")

Heart Attack
	n	%	val%
No	3110	87.4	92.8
Yes	238	6.7	7.1
Scantron_Error	2	0.1	0.1
NA	207	5.8	NA
Total	3557	100.0	100.0

  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")

Heart Attack Age
	n	%	val%
0	43	1.2	1.2
1	1	0.0	0.0
14	1	0.0	0.0
17	2	0.1	0.1
20	1	0.0	0.0
24	1	0.0	0.0
25	2	0.1	0.1
26	1	0.0	0.0
27	2	0.1	0.1
29	2	0.1	0.1
31	2	0.1	0.1
32	1	0.0	0.0
34	1	0.0	0.0
35	2	0.1	0.1
38	3	0.1	0.1
40	4	0.1	0.1
42	1	0.0	0.0
44	2	0.1	0.1
45	4	0.1	0.1
46	4	0.1	0.1
47	1	0.0	0.0
48	7	0.2	0.2
49	2	0.1	0.1
5	1	0.0	0.0
50	19	0.5	0.5
51	6	0.2	0.2
52	10	0.3	0.3
53	2	0.1	0.1
54	5	0.1	0.1
55	10	0.3	0.3
56	4	0.1	0.1
57	7	0.2	0.2
58	12	0.3	0.3
59	12	0.3	0.3
60	14	0.4	0.4
61	6	0.2	0.2
62	3	0.1	0.1
63	8	0.2	0.2
64	8	0.2	0.2
65	6	0.2	0.2
66	3	0.1	0.1
67	6	0.2	0.2
68	2	0.1	0.1
69	7	0.2	0.2
70	5	0.1	0.1
71	3	0.1	0.1
72	1	0.0	0.0
73	3	0.1	0.1
74	6	0.2	0.2
76	2	0.1	0.1
79	1	0.0	0.0
93	1	0.0	0.0
“NA”	3294	92.6	92.6
Total	3557	100.0	100.0

# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  levels(b4ba) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ba <- ordered(b4ba, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")

Heart Failure or CHF
	n	%	val%
No	3092	86.9	93.3
Yes	222	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	243	6.8	NA
Total	3557	100.0	100.0

  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")

Heart Failure or CHF Age
	n	%	val%
12	1	0.0	0.0
13	1	0.0	0.0
17	1	0.0	0.0
18	1	0.0	0.0
20	2	0.1	0.1
25	1	0.0	0.0
27	1	0.0	0.0
28	1	0.0	0.0
29	1	0.0	0.0
30	2	0.1	0.1
31	1	0.0	0.0
34	1	0.0	0.0
35	1	0.0	0.0
39	1	0.0	0.0
40	4	0.1	0.1
42	1	0.0	0.0
43	3	0.1	0.1
45	1	0.0	0.0
46	2	0.1	0.1
47	2	0.1	0.1
48	4	0.1	0.1
49	6	0.2	0.2
5	1	0.0	0.0
50	6	0.2	0.2
51	3	0.1	0.1
52	7	0.2	0.2
53	4	0.1	0.1
54	5	0.1	0.1
55	9	0.3	0.3
56	10	0.3	0.3
57	4	0.1	0.1
58	6	0.2	0.2
59	10	0.3	0.3
60	11	0.3	0.3
61	6	0.2	0.2
62	11	0.3	0.3
63	6	0.2	0.2
64	4	0.1	0.1
65	8	0.2	0.2
66	5	0.1	0.1
67	4	0.1	0.1
68	5	0.1	0.1
69	5	0.1	0.1
7	1	0.0	0.0
70	6	0.2	0.2
71	5	0.1	0.1
72	3	0.1	0.1
73	2	0.1	0.1
74	3	0.1	0.1
75	2	0.1	0.1
77	1	0.0	0.0
78	1	0.0	0.0
80	1	0.0	0.0
82	1	0.0	0.0
97	1	0.0	0.0
“NA”	3361	94.5	94.5
Total	3557	100.0	100.0

# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  levels(b4ca) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ca <- ordered(b4ca, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")

Stroke
	n	%	val%
No	3009	84.6	90.7
Yes	306	8.6	9.2
Scantron_Error	3	0.1	0.1
NA	239	6.7	NA
Total	3557	100.0	100.0

  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")

Stroke Age
	n	%	val%
0	67	1.9	1.9
15	1	0.0	0.0
16	1	0.0	0.0
19	1	0.0	0.0
20	1	0.0	0.0
23	1	0.0	0.0
25	1	0.0	0.0
27	1	0.0	0.0
30	2	0.1	0.1
34	2	0.1	0.1
35	1	0.0	0.0
38	1	0.0	0.0
40	4	0.1	0.1
42	4	0.1	0.1
43	3	0.1	0.1
45	7	0.2	0.2
46	4	0.1	0.1
47	2	0.1	0.1
48	4	0.1	0.1
49	1	0.0	0.0
5	3	0.1	0.1
50	11	0.3	0.3
51	5	0.1	0.1
52	7	0.2	0.2
53	3	0.1	0.1
54	11	0.3	0.3
55	11	0.3	0.3
56	7	0.2	0.2
57	12	0.3	0.3
58	7	0.2	0.2
59	10	0.3	0.3
60	16	0.4	0.4
61	14	0.4	0.4
62	11	0.3	0.3
63	19	0.5	0.5
64	9	0.3	0.3
65	15	0.4	0.4
66	9	0.3	0.3
67	7	0.2	0.2
68	10	0.3	0.3
69	10	0.3	0.3
70	6	0.2	0.2
71	3	0.1	0.1
72	2	0.1	0.1
73	2	0.1	0.1
74	3	0.1	0.1
75	1	0.0	0.0
78	2	0.1	0.1
79	3	0.1	0.1
8	1	0.0	0.0
“NA”	3218	90.5	90.5
Total	3557	100.0	100.0

# Hypertension 
  b4da <- as.factor(d[,"b4da"])
  levels(b4da) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4da <- ordered(b4da, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")

Hypertension
	n	%	val%
No	887	24.9	26.2
Yes	2496	70.2	73.6
Scantron_Error	7	0.2	0.2
NA	167	4.7	NA
Total	3557	100.0	100.0

  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")

Hypertension Age
	n	%	val%
.35	1	0.0	0.0
1	1	0.0	0.0
10	1	0.0	0.0
12	3	0.1	0.1
13	1	0.0	0.0
14	1	0.0	0.0
15	3	0.1	0.1
16	9	0.3	0.3
17	2	0.1	0.1
18	7	0.2	0.2
19	4	0.1	0.1
2	3	0.1	0.1
20	12	0.3	0.3
21	5	0.1	0.1
22	7	0.2	0.2
23	5	0.1	0.1
24	9	0.3	0.3
25	18	0.5	0.5
26	3	0.1	0.1
27	7	0.2	0.2
28	11	0.3	0.3
29	6	0.2	0.2
30	62	1.7	1.7
31	13	0.4	0.4
32	14	0.4	0.4
33	9	0.3	0.3
34	6	0.2	0.2
35	79	2.2	2.2
36	18	0.5	0.5
37	11	0.3	0.3
38	24	0.7	0.7
39	14	0.4	0.4
4	2	0.1	0.1
40	165	4.6	4.6
41	15	0.4	0.4
42	33	0.9	0.9
43	19	0.5	0.5
44	17	0.5	0.5
45	154	4.3	4.3
46	26	0.7	0.7
47	21	0.6	0.6
48	42	1.2	1.2
49	29	0.8	0.8
5	5	0.1	0.1
50	251	7.1	7.1
51	23	0.6	0.6
52	45	1.3	1.3
53	18	0.5	0.5
54	49	1.4	1.4
55	166	4.7	4.7
56	52	1.5	1.5
57	38	1.1	1.1
58	62	1.7	1.7
59	37	1.0	1.0
6	1	0.0	0.0
60	163	4.6	4.6
61	35	1.0	1.0
62	53	1.5	1.5
63	25	0.7	0.7
64	30	0.8	0.8
65	56	1.6	1.6
66	15	0.4	0.4
67	22	0.6	0.6
68	25	0.7	0.7
69	17	0.5	0.5
7	2	0.1	0.1
70	27	0.8	0.8
71	13	0.4	0.4
72	10	0.3	0.3
73	5	0.1	0.1
74	3	0.1	0.1
75	4	0.1	0.1
77	1	0.0	0.0
78	1	0.0	0.0
79	1	0.0	0.0
8	3	0.1	0.1
80	2	0.1	0.1
89	2	0.1	0.1
9	3	0.1	0.1
92	1	0.0	0.0
94	2	0.1	0.1
96	1	0.0	0.0
98	1	0.0	0.0
99	4	0.1	0.1
“NA”	1391	39.1	39.1
Total	3557	100.0	100.0

# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
  levels(b4ea) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ea <- ordered(b4ea, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")

Peripheral arterial disease
	n	%	val%
No	3031	85.2	93.3
Yes	215	6.0	6.6
Scantron_Error	2	0.1	0.1
NA	309	8.7	NA
Total	3557	100.0	100.0

  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")

Peripheral arterial disease Age
	n	%	val%
*	1	0.0	0.0
**	1	0.0	0.0
0	23	0.6	0.6
1	1	0.0	0.0
16	1	0.0	0.0
17	1	0.0	0.0
19	1	0.0	0.0
25	1	0.0	0.0
26	1	0.0	0.0
30	2	0.1	0.1
31	1	0.0	0.0
33	1	0.0	0.0
34	2	0.1	0.1
35	4	0.1	0.1
36	1	0.0	0.0
37	1	0.0	0.0
40	12	0.3	0.3
41	1	0.0	0.0
42	1	0.0	0.0
44	2	0.1	0.1
45	5	0.1	0.1
46	1	0.0	0.0
47	1	0.0	0.0
48	7	0.2	0.2
5	1	0.0	0.0
50	19	0.5	0.5
51	4	0.1	0.1
52	1	0.0	0.0
53	2	0.1	0.1
54	6	0.2	0.2
55	13	0.4	0.4
56	5	0.1	0.1
57	6	0.2	0.2
58	12	0.3	0.3
59	5	0.1	0.1
60	22	0.6	0.6
61	2	0.1	0.1
62	11	0.3	0.3
63	3	0.1	0.1
64	7	0.2	0.2
65	15	0.4	0.4
66	5	0.1	0.1
67	5	0.1	0.1
68	7	0.2	0.2
69	6	0.2	0.2
70	9	0.3	0.3
71	2	0.1	0.1
72	3	0.1	0.1
73	3	0.1	0.1
74	3	0.1	0.1
76	1	0.0	0.0
77	1	0.0	0.0
78	2	0.1	0.1
82	1	0.0	0.0
84	1	0.0	0.0
94	1	0.0	0.0
97	1	0.0	0.0
“NA”	3299	92.7	92.7
Total	3557	100.0	100.0

# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  levels(b4fa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4fa <- ordered(b4fa, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")

High Cholesterol
	n	%	val%
No	1502	42.2	45.1
Yes	1828	51.4	54.8
Scantron_Error	3	0.1	0.1
NA	224	6.3	NA
Total	3557	100.0	100.0

  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")

High Cholesterol Age
	n	%	val%
1	3	0.1	0.1
10	5	0.1	0.1
12	2	0.1	0.1
14	1	0.0	0.0
15	1	0.0	0.0
16	2	0.1	0.1
17	1	0.0	0.0
18	1	0.0	0.0
19	4	0.1	0.1
2	1	0.0	0.0
20	3	0.1	0.1
21	2	0.1	0.1
22	1	0.0	0.0
24	5	0.1	0.1
25	4	0.1	0.1
26	2	0.1	0.1
27	3	0.1	0.1
28	6	0.2	0.2
29	2	0.1	0.1
30	14	0.4	0.4
31	4	0.1	0.1
32	6	0.2	0.2
33	1	0.0	0.0
34	7	0.2	0.2
35	35	1.0	1.0
36	15	0.4	0.4
37	4	0.1	0.1
38	16	0.4	0.4
39	7	0.2	0.2
4	1	0.0	0.0
40	74	2.1	2.1
41	8	0.2	0.2
42	14	0.4	0.4
43	9	0.3	0.3
44	11	0.3	0.3
45	86	2.4	2.4
46	14	0.4	0.4
47	13	0.4	0.4
48	29	0.8	0.8
49	12	0.3	0.3
5	5	0.1	0.1
50	174	4.9	4.9
51	19	0.5	0.5
52	29	0.8	0.8
53	19	0.5	0.5
54	37	1.0	1.0
55	134	3.8	3.8
56	37	1.0	1.0
57	42	1.2	1.2
58	39	1.1	1.1
59	32	0.9	0.9
6	2	0.1	0.1
60	138	3.9	3.9
61	24	0.7	0.7
62	57	1.6	1.6
63	29	0.8	0.8
64	25	0.7	0.7
65	53	1.5	1.5
66	13	0.4	0.4
67	16	0.4	0.4
68	23	0.6	0.6
69	25	0.7	0.7
7	2	0.1	0.1
70	31	0.9	0.9
71	6	0.2	0.2
72	13	0.4	0.4
73	6	0.2	0.2
74	9	0.3	0.3
75	8	0.2	0.2
76	3	0.1	0.1
77	1	0.0	0.0
8	1	0.0	0.0
80	1	0.0	0.0
86	1	0.0	0.0
9	1	0.0	0.0
92	1	0.0	0.0
94	1	0.0	0.0
95	1	0.0	0.0
96	1	0.0	0.0
97	1	0.0	0.0
99	2	0.1	0.1
“NA”	2066	58.1	58.1
Total	3557	100.0	100.0

#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  levels(b4ga) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ga <- ordered(b4ga, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")

Asthma, COPD
	n	%	val%
No	2927	82.3	84.6
Yes	529	14.9	15.3
Scantron_Error	2	0.1	0.1
NA	99	2.8	NA
Total	3557	100.0	100.0

  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")

Asthma, COPD Age
	n	%	val%
06	2	0.1	0.1
1	10	0.3	0.3
10	19	0.5	0.5
11	4	0.1	0.1
12	11	0.3	0.3
13	2	0.1	0.1
14	5	0.1	0.1
15	4	0.1	0.1
16	3	0.1	0.1
17	2	0.1	0.1
18	9	0.3	0.3
19	3	0.1	0.1
2	6	0.2	0.2
20	6	0.2	0.2
21	2	0.1	0.1
23	1	0.0	0.0
25	6	0.2	0.2
26	1	0.0	0.0
27	3	0.1	0.1
28	2	0.1	0.1
29	1	0.0	0.0
3	2	0.1	0.1
30	8	0.2	0.2
31	1	0.0	0.0
32	3	0.1	0.1
33	2	0.1	0.1
35	4	0.1	0.1
37	1	0.0	0.0
38	3	0.1	0.1
39	1	0.0	0.0
4	10	0.3	0.3
40	10	0.3	0.3
42	4	0.1	0.1
43	1	0.0	0.0
45	9	0.3	0.3
46	1	0.0	0.0
47	1	0.0	0.0
48	4	0.1	0.1
49	2	0.1	0.1
5	28	0.8	0.8
50	24	0.7	0.7
51	4	0.1	0.1
52	6	0.2	0.2
53	2	0.1	0.1
54	3	0.1	0.1
55	16	0.4	0.4
56	12	0.3	0.3
57	13	0.4	0.4
58	9	0.3	0.3
59	10	0.3	0.3
6	14	0.4	0.4
60	28	0.8	0.8
61	2	0.1	0.1
62	11	0.3	0.3
63	11	0.3	0.3
64	6	0.2	0.2
65	16	0.4	0.4
66	5	0.1	0.1
67	7	0.2	0.2
68	7	0.2	0.2
69	6	0.2	0.2
7	12	0.3	0.3
70	9	0.3	0.3
71	6	0.2	0.2
72	2	0.1	0.1
73	4	0.1	0.1
74	2	0.1	0.1
75	4	0.1	0.1
77	3	0.1	0.1
78	2	0.1	0.1
79	1	0.0	0.0
8	8	0.2	0.2
80	1	0.0	0.0
81	1	0.0	0.0
9	8	0.2	0.2
93	1	0.0	0.0
97	1	0.0	0.0
“NA”	3083	86.7	86.7
Total	3557	100.0	100.0

# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  levels(b4ha) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ha <- ordered(b4ha, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")

Stomach ulcers
	n	%	val%
No	3142	88.3	91.4
Yes	294	8.3	8.6
Scantron_Error	1	0.0	0.0
NA	120	3.4	NA
Total	3557	100.0	100.0

  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")

Stomach ulcers Age
	n	%	val%
0	46	1.3	1.3
10	2	0.1	0.1
13	2	0.1	0.1
14	3	0.1	0.1
15	3	0.1	0.1
16	4	0.1	0.1
17	1	0.0	0.0
18	4	0.1	0.1
19	3	0.1	0.1
2	1	0.0	0.0
20	4	0.1	0.1
21	1	0.0	0.0
22	7	0.2	0.2
23	3	0.1	0.1
24	4	0.1	0.1
25	9	0.3	0.3
27	3	0.1	0.1
28	4	0.1	0.1
3	1	0.0	0.0
30	12	0.3	0.3
32	3	0.1	0.1
34	3	0.1	0.1
35	17	0.5	0.5
36	4	0.1	0.1
38	1	0.0	0.0
39	3	0.1	0.1
40	14	0.4	0.4
42	3	0.1	0.1
44	2	0.1	0.1
45	15	0.4	0.4
46	2	0.1	0.1
47	3	0.1	0.1
48	6	0.2	0.2
49	1	0.0	0.0
50	14	0.4	0.4
52	2	0.1	0.1
53	1	0.0	0.0
54	1	0.0	0.0
55	5	0.1	0.1
56	2	0.1	0.1
57	4	0.1	0.1
58	4	0.1	0.1
59	3	0.1	0.1
60	11	0.3	0.3
61	1	0.0	0.0
62	9	0.3	0.3
63	5	0.1	0.1
64	3	0.1	0.1
65	7	0.2	0.2
66	3	0.1	0.1
67	5	0.1	0.1
68	3	0.1	0.1
69	2	0.1	0.1
7	1	0.0	0.0
70	2	0.1	0.1
71	1	0.0	0.0
72	3	0.1	0.1
73	1	0.0	0.0
74	1	0.0	0.0
76	1	0.0	0.0
8	1	0.0	0.0
80	1	0.0	0.0
9	2	0.1	0.1
94	1	0.0	0.0
“NA”	3263	91.7	91.7
Total	3557	100.0	100.0

# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  levels(b4ia) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ia <- ordered(b4ia, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")

Crohn’s Disease
	n	%	val%
No	3314	93.2	96.7
Yes	114	3.2	3.3
Scantron_Error	0	0.0	0.0
NA	129	3.6	NA
Total	3557	100.0	100.0

  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")

Crohn’s Disease Age
	n	%	val%
15	1	0.0	0.0
2	2	0.1	0.1
22	2	0.1	0.1
30	1	0.0	0.0
32	3	0.1	0.1
33	1	0.0	0.0
34	1	0.0	0.0
35	2	0.1	0.1
37	1	0.0	0.0
39	1	0.0	0.0
40	11	0.3	0.3
41	1	0.0	0.0
44	5	0.1	0.1
45	2	0.1	0.1
46	1	0.0	0.0
48	2	0.1	0.1
50	8	0.2	0.2
51	3	0.1	0.1
52	3	0.1	0.1
53	1	0.0	0.0
54	2	0.1	0.1
55	8	0.2	0.2
56	2	0.1	0.1
57	1	0.0	0.0
58	5	0.1	0.1
59	1	0.0	0.0
60	6	0.2	0.2
62	1	0.0	0.0
63	2	0.1	0.1
64	1	0.0	0.0
65	3	0.1	0.1
66	2	0.1	0.1
67	1	0.0	0.0
69	3	0.1	0.1
70	2	0.1	0.1
71	2	0.1	0.1
74	1	0.0	0.0
75	2	0.1	0.1
76	2	0.1	0.1
79	1	0.0	0.0
85	1	0.0	0.0
“NA”	3456	97.2	97.2
Total	3557	100.0	100.0

# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  levels(b4ja) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ja <- ordered(b4ja, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")

Diabetes
	n	%	val%
No	2383	67.0	68.6
Yes	1086	30.5	31.3
Scantron_Error	4	0.1	0.1
NA	84	2.4	NA
Total	3557	100.0	100.0

  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")

Diabetes Age
	n	%	val%
0	10	0.3	0.3
10	1	0.0	0.0
12	2	0.1	0.1
13	2	0.1	0.1
14	2	0.1	0.1
15	2	0.1	0.1
16	1	0.0	0.0
17	4	0.1	0.1
18	1	0.0	0.0
19	1	0.0	0.0
2	1	0.0	0.0
20	3	0.1	0.1
22	1	0.0	0.0
24	2	0.1	0.1
27	1	0.0	0.0
28	2	0.1	0.1
29	5	0.1	0.1
3	2	0.1	0.1
30	14	0.4	0.4
31	1	0.0	0.0
32	1	0.0	0.0
34	1	0.0	0.0
35	21	0.6	0.6
36	3	0.1	0.1
37	5	0.1	0.1
38	7	0.2	0.2
39	6	0.2	0.2
40	37	1.0	1.0
41	4	0.1	0.1
42	13	0.4	0.4
43	12	0.3	0.3
44	8	0.2	0.2
45	49	1.4	1.4
46	8	0.2	0.2
47	12	0.3	0.3
48	17	0.5	0.5
49	8	0.2	0.2
5	1	0.0	0.0
50	79	2.2	2.2
51	13	0.4	0.4
52	16	0.4	0.4
53	20	0.6	0.6
54	28	0.8	0.8
55	78	2.2	2.2
56	28	0.8	0.8
57	22	0.6	0.6
58	39	1.1	1.1
59	28	0.8	0.8
60	75	2.1	2.1
61	29	0.8	0.8
62	37	1.0	1.0
63	17	0.5	0.5
64	21	0.6	0.6
65	40	1.1	1.1
66	14	0.4	0.4
67	5	0.1	0.1
68	13	0.4	0.4
69	16	0.4	0.4
70	24	0.7	0.7
71	6	0.2	0.2
72	4	0.1	0.1
73	2	0.1	0.1
74	3	0.1	0.1
75	5	0.1	0.1
76	2	0.1	0.1
78	1	0.0	0.0
81	1	0.0	0.0
94	1	0.0	0.0
95	1	0.0	0.0
97	1	0.0	0.0
“NA”	2617	73.6	73.6
Total	3557	100.0	100.0

# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  levels(b4ka) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ka <- ordered(b4ka, c("No", "Yes", "Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")

Kidney Problems
	n	%	val%
No	3253	91.5	94.0
Yes	206	5.8	6.0
Scantron_Error	2	0.1	0.1
NA	96	2.7	NA
Total	3557	100.0	100.0

  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")

Kidney Problems Age
	n	%	val%
11	1	0.0	0.0
15	3	0.1	0.1
16	1	0.0	0.0
17	1	0.0	0.0
29	1	0.0	0.0
35	2	0.1	0.1
38	1	0.0	0.0
40	5	0.1	0.1
42	1	0.0	0.0
44	1	0.0	0.0
45	3	0.1	0.1
46	1	0.0	0.0
47	1	0.0	0.0
48	1	0.0	0.0
49	1	0.0	0.0
5	2	0.1	0.1
50	11	0.3	0.3
51	3	0.1	0.1
52	2	0.1	0.1
53	4	0.1	0.1
54	5	0.1	0.1
55	11	0.3	0.3
56	6	0.2	0.2
57	4	0.1	0.1
58	4	0.1	0.1
59	5	0.1	0.1
60	6	0.2	0.2
61	5	0.1	0.1
62	8	0.2	0.2
63	1	0.0	0.0
64	5	0.1	0.1
65	11	0.3	0.3
66	4	0.1	0.1
67	6	0.2	0.2
68	6	0.2	0.2
69	5	0.1	0.1
7	1	0.0	0.0
70	7	0.2	0.2
71	3	0.1	0.1
72	3	0.1	0.1
74	2	0.1	0.1
75	4	0.1	0.1
76	1	0.0	0.0
77	2	0.1	0.1
80	1	0.0	0.0
83	1	0.0	0.0
95	1	0.0	0.0
“NA”	3393	95.4	95.4
Total	3557	100.0	100.0

# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  levels(b4la) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4la <- ordered(b4la, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")

Cirrhosis, liver damage
	n	%	val%
No	3376	94.9	97.9
Yes	71	2.0	2.1
Scantron_Error	0	0.0	0.0
NA	110	3.1	NA
Total	3557	100.0	100.0

  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")

Cirrhosis, liver damage Age
	n	%	val%
18	1	0.0	0.0
21	1	0.0	0.0
39	1	0.0	0.0
40	1	0.0	0.0
42	1	0.0	0.0
45	5	0.1	0.1
47	1	0.0	0.0
48	2	0.1	0.1
49	1	0.0	0.0
50	2	0.1	0.1
51	2	0.1	0.1
53	1	0.0	0.0
54	2	0.1	0.1
55	3	0.1	0.1
58	1	0.0	0.0
60	8	0.2	0.2
61	1	0.0	0.0
63	1	0.0	0.0
64	1	0.0	0.0
65	3	0.1	0.1
66	3	0.1	0.1
67	2	0.1	0.1
68	2	0.1	0.1
69	2	0.1	0.1
74	1	0.0	0.0
77	1	0.0	0.0
“NA”	3507	98.6	98.6
Total	3557	100.0	100.0

# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  levels(b4ma) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4ma <- ordered(b4ma, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")

Arthritis
	n	%	val%
No	3006	84.5	87.3
Yes	436	12.3	12.7
Scantron_Error	2	0.1	0.1
NA	113	3.2	NA
Total	3557	100.0	100.0

  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")

Arthritis Age
	n	%	val%
1	1	0.0	0.0
10	1	0.0	0.0
17	1	0.0	0.0
19	1	0.0	0.0
2	1	0.0	0.0
20	2	0.1	0.1
21	1	0.0	0.0
22	1	0.0	0.0
23	1	0.0	0.0
24	1	0.0	0.0
25	3	0.1	0.1
28	1	0.0	0.0
30	9	0.3	0.3
32	2	0.1	0.1
33	1	0.0	0.0
35	7	0.2	0.2
36	2	0.1	0.1
38	2	0.1	0.1
39	3	0.1	0.1
4	2	0.1	0.1
40	19	0.5	0.5
41	5	0.1	0.1
42	4	0.1	0.1
43	4	0.1	0.1
44	4	0.1	0.1
45	17	0.5	0.5
46	2	0.1	0.1
47	4	0.1	0.1
48	7	0.2	0.2
49	2	0.1	0.1
50	37	1.0	1.0
51	7	0.2	0.2
52	8	0.2	0.2
53	6	0.2	0.2
54	9	0.3	0.3
55	32	0.9	0.9
56	7	0.2	0.2
57	7	0.2	0.2
58	14	0.4	0.4
59	2	0.1	0.1
60	34	1.0	1.0
61	5	0.1	0.1
62	10	0.3	0.3
63	8	0.2	0.2
64	9	0.3	0.3
65	16	0.4	0.4
66	2	0.1	0.1
67	6	0.2	0.2
68	7	0.2	0.2
69	5	0.1	0.1
7	1	0.0	0.0
70	6	0.2	0.2
71	3	0.1	0.1
72	5	0.1	0.1
73	1	0.0	0.0
74	1	0.0	0.0
75	3	0.1	0.1
76	1	0.0	0.0
77	1	0.0	0.0
80	1	0.0	0.0
“NA”	3192	89.7	89.7
Total	3557	100.0	100.0

# Dementia
  b4na <- as.factor(d[,"b4na"])
  levels(b4na) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4na <- ordered(b4na, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")

Dementia
	n	%	val%
No	3420	96.1	98.7
Yes	45	1.3	1.3
Scantron_Error	1	0.0	0.0
NA	91	2.6	NA
Total	3557	100.0	100.0

  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")

Dementia Age
	n	%	val%
10	1	0.0	0.0
29	1	0.0	0.0
53	1	0.0	0.0
57	1	0.0	0.0
60	1	0.0	0.0
62	2	0.1	0.1
63	1	0.0	0.0
64	1	0.0	0.0
65	2	0.1	0.1
66	1	0.0	0.0
67	1	0.0	0.0
69	1	0.0	0.0
70	6	0.2	0.2
72	3	0.1	0.1
73	2	0.1	0.1
74	1	0.0	0.0
75	1	0.0	0.0
80	1	0.0	0.0
“NA”	3529	99.2	99.2
Total	3557	100.0	100.0

# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  levels(b4oa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4oa <- ordered(b4oa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")

Depression
	n	%	val%
No	2992	84.1	87.0
Yes	447	12.6	13.0
Scantron_Error	2	0.1	0.1
NA	116	3.3	NA
Total	3557	100.0	100.0

  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")

Depression Age
	n	%	val%
1	1	0.0	0.0
14	1	0.0	0.0
16	1	0.0	0.0
17	1	0.0	0.0
18	1	0.0	0.0
19	6	0.2	0.2
20	6	0.2	0.2
21	5	0.1	0.1
22	1	0.0	0.0
24	1	0.0	0.0
25	4	0.1	0.1
26	3	0.1	0.1
28	6	0.2	0.2
29	2	0.1	0.1
30	5	0.1	0.1
32	4	0.1	0.1
33	1	0.0	0.0
34	1	0.0	0.0
35	10	0.3	0.3
36	4	0.1	0.1
37	4	0.1	0.1
38	3	0.1	0.1
39	1	0.0	0.0
4	1	0.0	0.0
40	15	0.4	0.4
41	2	0.1	0.1
42	8	0.2	0.2
43	5	0.1	0.1
44	3	0.1	0.1
45	12	0.3	0.3
46	3	0.1	0.1
47	7	0.2	0.2
48	7	0.2	0.2
49	6	0.2	0.2
50	27	0.8	0.8
51	3	0.1	0.1
52	9	0.3	0.3
53	6	0.2	0.2
54	5	0.1	0.1
55	22	0.6	0.6
56	7	0.2	0.2
57	6	0.2	0.2
58	9	0.3	0.3
59	7	0.2	0.2
6	1	0.0	0.0
60	17	0.5	0.5
61	10	0.3	0.3
62	6	0.2	0.2
63	5	0.1	0.1
64	6	0.2	0.2
65	15	0.4	0.4
66	6	0.2	0.2
67	4	0.1	0.1
68	8	0.2	0.2
69	1	0.0	0.0
7	2	0.1	0.1
70	9	0.3	0.3
72	3	0.1	0.1
73	2	0.1	0.1
74	2	0.1	0.1
75	1	0.0	0.0
76	1	0.0	0.0
8	1	0.0	0.0
91	1	0.0	0.0
96	1	0.0	0.0
98	2	0.1	0.1
“NA”	3211	90.3	90.3
Total	3557	100.0	100.0

# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  levels(b4pa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4pa <- ordered(b4pa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")

AIDS
	n	%	val%
No	3408	95.8	98.8
Yes	41	1.2	1.2
Scantron_Error	0	0.0	0.0
NA	108	3.0	NA
Total	3557	100.0	100.0

  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")

AIDS Age
	n	%	val%
0	47	1.3	1.3
25	1	0.0	0.0
30	1	0.0	0.0
33	1	0.0	0.0
36	2	0.1	0.1
38	1	0.0	0.0
39	1	0.0	0.0
40	3	0.1	0.1
45	1	0.0	0.0
48	1	0.0	0.0
49	2	0.1	0.1
50	2	0.1	0.1
51	1	0.0	0.0
55	1	0.0	0.0
59	2	0.1	0.1
60	1	0.0	0.0
63	1	0.0	0.0
65	1	0.0	0.0
9	1	0.0	0.0
90	1	0.0	0.0
“NA”	3485	98.0	98.0
Total	3557	100.0	100.0

# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  levels(b4qa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  b4qa <- ordered(b4qa, c("No", "Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")

Other Cancer
	n	%	val%
No	3172	89.2	93.3
Yes	228	6.4	6.7
Scantron_Error	1	0.0	0.0
NA	156	4.4	NA
Total	3557	100.0	100.0

  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")

Other Cancer Age
	n	%	val%
10	1	0.0	0.0
12	1	0.0	0.0
16	1	0.0	0.0
18	1	0.0	0.0
19	1	0.0	0.0
2	1	0.0	0.0
20	1	0.0	0.0
22	1	0.0	0.0
24	1	0.0	0.0
30	1	0.0	0.0
35	1	0.0	0.0
36	1	0.0	0.0
38	1	0.0	0.0
39	1	0.0	0.0
40	2	0.1	0.1
41	1	0.0	0.0
42	3	0.1	0.1
43	3	0.1	0.1
44	4	0.1	0.1
45	1	0.0	0.0
47	2	0.1	0.1
48	1	0.0	0.0
49	6	0.2	0.2
50	7	0.2	0.2
51	3	0.1	0.1
52	2	0.1	0.1
54	7	0.2	0.2
55	7	0.2	0.2
56	7	0.2	0.2
57	3	0.1	0.1
58	12	0.3	0.3
59	4	0.1	0.1
60	13	0.4	0.4
61	11	0.3	0.3
62	10	0.3	0.3
63	10	0.3	0.3
64	5	0.1	0.1
65	6	0.2	0.2
66	8	0.2	0.2
67	6	0.2	0.2
68	8	0.2	0.2
69	8	0.2	0.2
7	1	0.0	0.0
70	6	0.2	0.2
71	3	0.1	0.1
72	4	0.1	0.1
73	2	0.1	0.1
74	6	0.2	0.2
75	2	0.1	0.1
76	1	0.0	0.0
77	1	0.0	0.0
78	1	0.0	0.0
79	1	0.0	0.0
“NA”	3355	94.3	94.3
Total	3557	100.0	100.0

LA County

Heart Attack
	n	%	val%
No	291	90.7	92.4
Yes	24	7.5	7.6
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

Heart Attack age
	n	%	val%
0	0	0.0	0.0
1	0	0.0	0.0
14	0	0.0	0.0
17	0	0.0	0.0
20	0	0.0	0.0
24	0	0.0	0.0
25	1	0.3	0.3
26	0	0.0	0.0
27	0	0.0	0.0
29	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	1	0.3	0.3
40	1	0.3	0.3
42	1	0.3	0.3
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
5	0	0.0	0.0
50	3	0.9	0.9
51	1	0.3	0.3
52	2	0.6	0.6
53	0	0.0	0.0
54	2	0.6	0.6
55	2	0.6	0.6
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.3	0.3
59	0	0.0	0.0
60	2	0.6	0.6
61	1	0.3	0.3
62	0	0.0	0.0
63	1	0.3	0.3
64	1	0.3	0.3
65	1	0.3	0.3
66	1	0.3	0.3
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	1	0.3	0.3
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
79	0	0.0	0.0
93	0	0.0	0.0
“NA”	297	92.5	92.5
Total	321	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	294	91.6	93.3
Yes	21	6.5	6.7
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	1	0.3	0.3
13	1	0.3	0.3
17	0	0.0	0.0
18	0	0.0	0.0
20	0	0.0	0.0
25	1	0.3	0.3
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	1	0.3	0.3
42	1	0.3	0.3
43	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	1	0.3	0.3
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	2	0.6	0.6
61	0	0.0	0.0
62	1	0.3	0.3
63	1	0.3	0.3
64	1	0.3	0.3
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.3	0.3
71	2	0.6	0.6
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
80	0	0.0	0.0
82	0	0.0	0.0
97	0	0.0	0.0
“NA”	302	94.1	94.1
Total	321	100.0	100.0

Stroke
	n	%	val%
No	289	90.0	92.3
Yes	24	7.5	7.7
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

Stroke age
	n	%	val%
0	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
30	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	1	0.3	0.3
45	2	0.6	0.6
46	1	0.3	0.3
47	1	0.3	0.3
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	1	0.3	0.3
51	0	0.0	0.0
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.3	0.3
58	1	0.3	0.3
59	0	0.0	0.0
60	1	0.3	0.3
61	2	0.6	0.6
62	0	0.0	0.0
63	3	0.9	0.9
64	1	0.3	0.3
65	1	0.3	0.3
66	2	0.6	0.6
67	1	0.3	0.3
68	1	0.3	0.3
69	2	0.6	0.6
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
“NA”	298	92.8	92.8
Total	321	100.0	100.0

Hypertension
	n	%	val%
No	99	30.8	31.5
Yes	215	67.0	68.5
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	0	0.0	0.0
10	1	0.3	0.3
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	1	0.3	0.3
19	0	0.0	0.0
2	1	0.3	0.3
20	0	0.0	0.0
21	0	0.0	0.0
22	1	0.3	0.3
23	0	0.0	0.0
24	1	0.3	0.3
25	2	0.6	0.6
26	0	0.0	0.0
27	1	0.3	0.3
28	0	0.0	0.0
29	1	0.3	0.3
30	5	1.6	1.6
31	0	0.0	0.0
32	6	1.9	1.9
33	0	0.0	0.0
34	1	0.3	0.3
35	8	2.5	2.5
36	0	0.0	0.0
37	3	0.9	0.9
38	6	1.9	1.9
39	2	0.6	0.6
4	0	0.0	0.0
40	12	3.7	3.7
41	3	0.9	0.9
42	7	2.2	2.2
43	2	0.6	0.6
44	0	0.0	0.0
45	20	6.2	6.2
46	3	0.9	0.9
47	1	0.3	0.3
48	6	1.9	1.9
49	2	0.6	0.6
5	0	0.0	0.0
50	16	5.0	5.0
51	2	0.6	0.6
52	8	2.5	2.5
53	1	0.3	0.3
54	0	0.0	0.0
55	15	4.7	4.7
56	7	2.2	2.2
57	3	0.9	0.9
58	8	2.5	2.5
59	1	0.3	0.3
6	0	0.0	0.0
60	11	3.4	3.4
61	4	1.2	1.2
62	6	1.9	1.9
63	2	0.6	0.6
64	0	0.0	0.0
65	9	2.8	2.8
66	0	0.0	0.0
67	0	0.0	0.0
68	4	1.2	1.2
69	1	0.3	0.3
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	2	0.6	0.6
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
89	0	0.0	0.0
9	1	0.3	0.3
92	0	0.0	0.0
94	1	0.3	0.3
96	0	0.0	0.0
98	1	0.3	0.3
99	0	0.0	0.0
“NA”	122	38.0	38.0
Total	321	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	301	93.8	96.8
Yes	10	3.1	3.2
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	0	0.0	0.0
1	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	1	0.3	0.3
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
5	0	0.0	0.0
50	0	0.0	0.0
51	1	0.3	0.3
52	0	0.0	0.0
53	0	0.0	0.0
54	1	0.3	0.3
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	3	0.9	0.9
59	0	0.0	0.0
60	1	0.3	0.3
61	0	0.0	0.0
62	2	0.6	0.6
63	0	0.0	0.0
64	2	0.6	0.6
65	3	0.9	0.9
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	1	0.3	0.3
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
82	0	0.0	0.0
84	0	0.0	0.0
94	0	0.0	0.0
97	0	0.0	0.0
“NA”	304	94.7	94.7
Total	321	100.0	100.0

High Cholesterol
	n	%	val%
No	141	43.9	44.9
Yes	172	53.6	54.8
Scantron_Error	1	0.3	0.3
NA	7	2.2	NA
Total	321	100.0	100.0

High Cholesterol age
	n	%	val%
1	0	0.0	0.0
10	1	0.3	0.3
12	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	1	0.3	0.3
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	1	0.3	0.3
33	0	0.0	0.0
34	1	0.3	0.3
35	4	1.2	1.2
36	1	0.3	0.3
37	0	0.0	0.0
38	1	0.3	0.3
39	2	0.6	0.6
4	0	0.0	0.0
40	6	1.9	1.9
41	1	0.3	0.3
42	5	1.6	1.6
43	2	0.6	0.6
44	1	0.3	0.3
45	8	2.5	2.5
46	0	0.0	0.0
47	0	0.0	0.0
48	4	1.2	1.2
49	0	0.0	0.0
5	1	0.3	0.3
50	18	5.6	5.6
51	2	0.6	0.6
52	7	2.2	2.2
53	0	0.0	0.0
54	1	0.3	0.3
55	16	5.0	5.0
56	1	0.3	0.3
57	2	0.6	0.6
58	14	4.4	4.4
59	2	0.6	0.6
6	0	0.0	0.0
60	14	4.4	4.4
61	1	0.3	0.3
62	6	1.9	1.9
63	3	0.9	0.9
64	4	1.2	1.2
65	2	0.6	0.6
66	0	0.0	0.0
67	1	0.3	0.3
68	3	0.9	0.9
69	4	1.2	1.2
7	0	0.0	0.0
70	1	0.3	0.3
71	1	0.3	0.3
72	2	0.6	0.6
73	1	0.3	0.3
74	2	0.6	0.6
75	1	0.3	0.3
76	0	0.0	0.0
77	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
86	0	0.0	0.0
9	1	0.3	0.3
92	0	0.0	0.0
94	1	0.3	0.3
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	0	0.0	0.0
“NA”	170	53.0	53.0
Total	321	100.0	100.0

Asthma, COPD
	n	%	val%
No	270	84.1	84.9
Yes	48	15.0	15.1
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	1	0.3	0.3
10	3	0.9	0.9
11	0	0.0	0.0
12	1	0.3	0.3
13	0	0.0	0.0
14	1	0.3	0.3
15	1	0.3	0.3
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	1	0.3	0.3
21	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	1	0.3	0.3
33	0	0.0	0.0
35	1	0.3	0.3
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	3	0.9	0.9
40	1	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
5	4	1.2	1.2
50	1	0.3	0.3
51	0	0.0	0.0
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.3	0.3
59	1	0.3	0.3
6	3	0.9	0.9
60	4	1.2	1.2
61	0	0.0	0.0
62	0	0.0	0.0
63	2	0.6	0.6
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.3	0.3
69	1	0.3	0.3
7	2	0.6	0.6
70	2	0.6	0.6
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	1	0.3	0.3
79	0	0.0	0.0
8	4	1.2	1.2
80	0	0.0	0.0
81	0	0.0	0.0
9	0	0.0	0.0
93	0	0.0	0.0
97	0	0.0	0.0
“NA”	275	85.7	85.7
Total	321	100.0	100.0

Stomach ulcers
	n	%	val%
No	290	90.3	91.5
Yes	27	8.4	8.5
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

Stomach ulcers age
	n	%	val%
0	0	0.0	0.0
10	1	0.3	0.3
13	1	0.3	0.3
14	0	0.0	0.0
15	1	0.3	0.3
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	2	0.6	0.6
23	1	0.3	0.3
24	0	0.0	0.0
25	2	0.6	0.6
27	0	0.0	0.0
28	2	0.6	0.6
3	1	0.3	0.3
30	0	0.0	0.0
32	1	0.3	0.3
34	1	0.3	0.3
35	1	0.3	0.3
36	0	0.0	0.0
38	1	0.3	0.3
39	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
44	1	0.3	0.3
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	2	0.6	0.6
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	1	0.3	0.3
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
9	0	0.0	0.0
94	0	0.0	0.0
“NA”	297	92.5	92.5
Total	321	100.0	100.0

Crohn’s Disease
	n	%	val%
No	304	94.7	96.2
Yes	12	3.7	3.8
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

Crohn’s Disease age
	n	%	val%
15	0	0.0	0.0
2	0	0.0	0.0
22	1	0.3	0.3
30	0	0.0	0.0
32	2	0.6	0.6
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
37	0	0.0	0.0
39	0	0.0	0.0
40	2	0.6	0.6
41	1	0.3	0.3
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
48	0	0.0	0.0
50	2	0.6	0.6
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	2	0.6	0.6
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.3	0.3
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
79	0	0.0	0.0
85	0	0.0	0.0
“NA”	310	96.6	96.6
Total	321	100.0	100.0

Diabetes
	n	%	val%
No	228	71.0	72.2
Yes	88	27.4	27.8
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

Diabetes age
	n	%	val%
0	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	2	0.6	0.6
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	1	0.3	0.3
30	2	0.6	0.6
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	1	0.3	0.3
36	0	0.0	0.0
37	0	0.0	0.0
38	1	0.3	0.3
39	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	3	0.9	0.9
43	0	0.0	0.0
44	1	0.3	0.3
45	8	2.5	2.5
46	0	0.0	0.0
47	0	0.0	0.0
48	2	0.6	0.6
49	0	0.0	0.0
5	0	0.0	0.0
50	3	0.9	0.9
51	2	0.6	0.6
52	2	0.6	0.6
53	2	0.6	0.6
54	4	1.2	1.2
55	6	1.9	1.9
56	1	0.3	0.3
57	1	0.3	0.3
58	5	1.6	1.6
59	1	0.3	0.3
60	4	1.2	1.2
61	4	1.2	1.2
62	7	2.2	2.2
63	1	0.3	0.3
64	1	0.3	0.3
65	5	1.6	1.6
66	4	1.2	1.2
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.3	0.3
70	0	0.0	0.0
71	1	0.3	0.3
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.3	0.3
76	1	0.3	0.3
78	0	0.0	0.0
81	0	0.0	0.0
94	1	0.3	0.3
95	0	0.0	0.0
97	0	0.0	0.0
“NA”	241	75.1	75.1
Total	321	100.0	100.0

Kidney Problems
	n	%	val%
No	302	94.1	95
Yes	16	5.0	5
Scantron_Error	0	0.0	0
NA	3	0.9	NA
Total	321	100.0	100

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
29	0	0.0	0.0
35	1	0.3	0.3
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	1	0.3	0.3
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	1	0.3	0.3
53	1	0.3	0.3
54	0	0.0	0.0
55	2	0.6	0.6
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	0.3	0.3
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	1	0.3	0.3
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.3	0.3
71	0	0.0	0.0
72	1	0.3	0.3
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	1	0.3	0.3
83	0	0.0	0.0
95	0	0.0	0.0
“NA”	306	95.3	95.3
Total	321	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	310	96.6	97.5
Yes	8	2.5	2.5
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	0	0.0	0.0
21	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
45	2	0.6	0.6
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	1	0.3	0.3
51	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
58	1	0.3	0.3
60	2	0.6	0.6
61	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.3	0.3
74	0	0.0	0.0
77	0	0.0	0.0
“NA”	314	97.8	97.8
Total	321	100.0	100.0

Arthritis
	n	%	val%
No	268	83.5	84.8
Yes	47	14.6	14.9
Scantron_Error	1	0.3	0.3
NA	5	1.6	NA
Total	321	100.0	100.0

Arthritis age
	n	%	val%
1	1	0.3	0.3
10	1	0.3	0.3
17	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
28	1	0.3	0.3
30	1	0.3	0.3
32	1	0.3	0.3
33	0	0.0	0.0
35	1	0.3	0.3
36	1	0.3	0.3
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	3	0.9	0.9
41	1	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	1	0.3	0.3
50	5	1.6	1.6
51	1	0.3	0.3
52	1	0.3	0.3
53	1	0.3	0.3
54	3	0.9	0.9
55	2	0.6	0.6
56	0	0.0	0.0
57	1	0.3	0.3
58	0	0.0	0.0
59	0	0.0	0.0
60	3	0.9	0.9
61	1	0.3	0.3
62	3	0.9	0.9
63	2	0.6	0.6
64	2	0.6	0.6
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	1	0.3	0.3
77	0	0.0	0.0
80	0	0.0	0.0
“NA”	280	87.2	87.2
Total	321	100.0	100.0

Dementia
	n	%	val%
No	317	98.8	99.4
Yes	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

Dementia age
	n	%	val%
10	0	0.0	0.0
29	0	0.0	0.0
53	0	0.0	0.0
57	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	1	0.3	0.3
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
80	0	0.0	0.0
“NA”	319	99.4	99.4
Total	321	100.0	100.0

Depression
	n	%	val%
No	276	86.0	86.8
Yes	42	13.1	13.2
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	1	0.3	0.3
24	0	0.0	0.0
25	2	0.6	0.6
26	0	0.0	0.0
28	1	0.3	0.3
29	1	0.3	0.3
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	3	0.9	0.9
36	0	0.0	0.0
37	1	0.3	0.3
38	0	0.0	0.0
39	1	0.3	0.3
4	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	1	0.3	0.3
43	0	0.0	0.0
44	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	1	0.3	0.3
51	1	0.3	0.3
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	4	1.2	1.2
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	1	0.3	0.3
6	0	0.0	0.0
60	1	0.3	0.3
61	2	0.6	0.6
62	0	0.0	0.0
63	0	0.0	0.0
64	2	0.6	0.6
65	2	0.6	0.6
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
72	1	0.3	0.3
73	1	0.3	0.3
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
“NA”	292	91.0	91.0
Total	321	100.0	100.0

AIDS
	n	%	val%
No	316	98.4	99.1
Yes	3	0.9	0.9
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

AIDS age
	n	%	val%
0	0	0.0	0.0
25	0	0.0	0.0
30	0	0.0	0.0
33	0	0.0	0.0
36	1	0.3	0.3
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
45	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
55	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
63	0	0.0	0.0
65	1	0.3	0.3
9	0	0.0	0.0
90	0	0.0	0.0
“NA”	319	99.4	99.4
Total	321	100.0	100.0

Other Cancer
	n	%	val%
No	296	92.2	94.9
Yes	16	5.0	5.1
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	0	0.0	0.0
16	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
30	0	0.0	0.0
35	1	0.3	0.3
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	1	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
44	1	0.3	0.3
45	0	0.0	0.0
47	1	0.3	0.3
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.3	0.3
59	0	0.0	0.0
60	2	0.6	0.6
61	0	0.0	0.0
62	2	0.6	0.6
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.3	0.3
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
7	1	0.3	0.3
70	0	0.0	0.0
71	0	0.0	0.0
72	1	0.3	0.3
73	1	0.3	0.3
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
“NA”	304	94.7	94.7
Total	321	100.0	100.0

Northern CA

Heart Attack
	n	%	val%
No	189	90.0	96.4
Yes	7	3.3	3.6
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

Heart Attack age
	n	%	val%
0	9	4.3	4.3
1	0	0.0	0.0
14	0	0.0	0.0
17	0	0.0	0.0
20	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
29	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	3	1.4	1.4
51	0	0.0	0.0
52	1	0.5	0.5
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	2	1.0	1.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
79	0	0.0	0.0
93	0	0.0	0.0
“NA”	195	92.9	92.9
Total	210	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	182	86.7	91.9
Yes	16	7.6	8.1
Scantron_Error	0	0.0	0.0
NA	12	5.7	NA
Total	210	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
20	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	1	0.5	0.5
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	1	0.5	0.5
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	1	0.5	0.5
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	1	0.5	0.5
60	2	1.0	1.0
61	0	0.0	0.0
62	1	0.5	0.5
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	1	0.5	0.5
68	1	0.5	0.5
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.5	0.5
77	1	0.5	0.5
78	1	0.5	0.5
80	0	0.0	0.0
82	0	0.0	0.0
97	0	0.0	0.0
“NA”	197	93.8	93.8
Total	210	100.0	100.0

Stroke
	n	%	val%
No	186	88.6	94.9
Yes	10	4.8	5.1
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

Stroke age
	n	%	val%
0	9	4.3	4.3
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
30	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	1	0.5	0.5
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	2	1.0	1.0
61	3	1.4	1.4
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.5	0.5
67	0	0.0	0.0
68	1	0.5	0.5
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
“NA”	192	91.4	91.4
Total	210	100.0	100.0

Hypertension
	n	%	val%
No	66	31.4	33.5
Yes	131	62.4	66.5
Scantron_Error	0	0.0	0.0
NA	13	6.2	NA
Total	210	100.0	100.0

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	1	0.5	0.5
20	1	0.5	0.5
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	1	0.5	0.5
26	0	0.0	0.0
27	0	0.0	0.0
28	1	0.5	0.5
29	1	0.5	0.5
30	2	1.0	1.0
31	1	0.5	0.5
32	1	0.5	0.5
33	0	0.0	0.0
34	0	0.0	0.0
35	3	1.4	1.4
36	0	0.0	0.0
37	1	0.5	0.5
38	1	0.5	0.5
39	0	0.0	0.0
4	0	0.0	0.0
40	12	5.7	5.7
41	1	0.5	0.5
42	0	0.0	0.0
43	3	1.4	1.4
44	1	0.5	0.5
45	5	2.4	2.4
46	1	0.5	0.5
47	3	1.4	1.4
48	3	1.4	1.4
49	1	0.5	0.5
5	0	0.0	0.0
50	18	8.6	8.6
51	1	0.5	0.5
52	1	0.5	0.5
53	0	0.0	0.0
54	1	0.5	0.5
55	10	4.8	4.8
56	1	0.5	0.5
57	1	0.5	0.5
58	4	1.9	1.9
59	3	1.4	1.4
6	0	0.0	0.0
60	14	6.7	6.7
61	4	1.9	1.9
62	3	1.4	1.4
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.5	0.5
66	1	0.5	0.5
67	1	0.5	0.5
68	1	0.5	0.5
69	1	0.5	0.5
7	1	0.5	0.5
70	1	0.5	0.5
71	0	0.0	0.0
72	1	0.5	0.5
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
89	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
99	0	0.0	0.0
“NA”	97	46.2	46.2
Total	210	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	189	90.0	96.9
Yes	5	2.4	2.6
Scantron_Error	1	0.5	0.5
NA	15	7.1	NA
Total	210	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	0	0.0	0.0
1	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
5	0	0.0	0.0
50	2	1.0	1.0
51	1	0.5	0.5
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	0.5	0.5
61	1	0.5	0.5
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
82	0	0.0	0.0
84	0	0.0	0.0
94	0	0.0	0.0
97	0	0.0	0.0
“NA”	204	97.1	97.1
Total	210	100.0	100.0

High Cholesterol
	n	%	val%
No	113	53.8	60.4
Yes	74	35.2	39.6
Scantron_Error	0	0.0	0.0
NA	23	11.0	NA
Total	210	100.0	100.0

High Cholesterol age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	2	1.0	1.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	3	1.4	1.4
41	1	0.5	0.5
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	5	2.4	2.4
46	0	0.0	0.0
47	0	0.0	0.0
48	2	1.0	1.0
49	0	0.0	0.0
5	0	0.0	0.0
50	7	3.3	3.3
51	1	0.5	0.5
52	1	0.5	0.5
53	0	0.0	0.0
54	2	1.0	1.0
55	8	3.8	3.8
56	2	1.0	1.0
57	2	1.0	1.0
58	0	0.0	0.0
59	1	0.5	0.5
6	0	0.0	0.0
60	10	4.8	4.8
61	2	1.0	1.0
62	2	1.0	1.0
63	3	1.4	1.4
64	0	0.0	0.0
65	3	1.4	1.4
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.5	0.5
69	0	0.0	0.0
7	1	0.5	0.5
70	3	1.4	1.4
71	0	0.0	0.0
72	1	0.5	0.5
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
86	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	0	0.0	0.0
“NA”	147	70.0	70.0
Total	210	100.0	100.0

Asthma, COPD
	n	%	val%
No	173	82.4	84
Yes	33	15.7	16
Scantron_Error	0	0.0	0
NA	4	1.9	NA
Total	210	100.0	100

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	0	0.0	0.0
10	2	1.0	1.0
11	0	0.0	0.0
12	0	0.0	0.0
13	1	0.5	0.5
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	1	0.5	0.5
20	1	0.5	0.5
21	0	0.0	0.0
23	0	0.0	0.0
25	1	0.5	0.5
26	0	0.0	0.0
27	1	0.5	0.5
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	1	0.5	0.5
31	0	0.0	0.0
32	0	0.0	0.0
33	1	0.5	0.5
35	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	2	1.0	1.0
40	1	0.5	0.5
42	0	0.0	0.0
43	0	0.0	0.0
45	1	0.5	0.5
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	2	1.0	1.0
50	3	1.4	1.4
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	1	0.5	0.5
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.5	0.5
58	0	0.0	0.0
59	1	0.5	0.5
6	0	0.0	0.0
60	2	1.0	1.0
61	0	0.0	0.0
62	1	0.5	0.5
63	0	0.0	0.0
64	0	0.0	0.0
65	2	1.0	1.0
66	0	0.0	0.0
67	1	0.5	0.5
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	1	0.5	0.5
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.5	0.5
77	1	0.5	0.5
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
81	0	0.0	0.0
9	1	0.5	0.5
93	0	0.0	0.0
97	0	0.0	0.0
“NA”	179	85.2	85.2
Total	210	100.0	100.0

Stomach ulcers
	n	%	val%
No	196	93.3	96.6
Yes	7	3.3	3.4
Scantron_Error	0	0.0	0.0
NA	7	3.3	NA
Total	210	100.0	100.0

Stomach ulcers age
	n	%	val%
0	9	4.3	4.3
10	0	0.0	0.0
13	1	0.5	0.5
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	1	0.5	0.5
44	0	0.0	0.0
45	1	0.5	0.5
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.5	0.5
59	0	0.0	0.0
60	1	0.5	0.5
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.5	0.5
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
9	0	0.0	0.0
94	0	0.0	0.0
“NA”	195	92.9	92.9
Total	210	100.0	100.0

Crohn’s Disease
	n	%	val%
No	192	91.4	94.6
Yes	11	5.2	5.4
Scantron_Error	0	0.0	0.0
NA	7	3.3	NA
Total	210	100.0	100.0

Crohn’s Disease age
	n	%	val%
15	0	0.0	0.0
2	0	0.0	0.0
22	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
37	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
44	1	0.5	0.5
45	1	0.5	0.5
46	0	0.0	0.0
48	1	0.5	0.5
50	2	1.0	1.0
51	1	0.5	0.5
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.5	0.5
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	1	0.5	0.5
71	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	1	0.5	0.5
79	0	0.0	0.0
85	0	0.0	0.0
“NA”	200	95.2	95.2
Total	210	100.0	100.0

Diabetes
	n	%	val%
No	154	73.3	75.1
Yes	51	24.3	24.9
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

Diabetes age
	n	%	val%
0	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	1	0.5	0.5
22	0	0.0	0.0
24	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	1	0.5	0.5
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	2	1.0	1.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	1	0.5	0.5
45	3	1.4	1.4
46	1	0.5	0.5
47	0	0.0	0.0
48	1	0.5	0.5
49	0	0.0	0.0
5	0	0.0	0.0
50	3	1.4	1.4
51	0	0.0	0.0
52	0	0.0	0.0
53	2	1.0	1.0
54	0	0.0	0.0
55	5	2.4	2.4
56	4	1.9	1.9
57	1	0.5	0.5
58	2	1.0	1.0
59	1	0.5	0.5
60	6	2.9	2.9
61	3	1.4	1.4
62	0	0.0	0.0
63	1	0.5	0.5
64	2	1.0	1.0
65	1	0.5	0.5
66	1	0.5	0.5
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.5	0.5
70	0	0.0	0.0
71	0	0.0	0.0
72	1	0.5	0.5
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.5	0.5
76	0	0.0	0.0
78	0	0.0	0.0
81	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
97	0	0.0	0.0
“NA”	165	78.6	78.6
Total	210	100.0	100.0

Kidney Problems
	n	%	val%
No	191	91.0	93.2
Yes	14	6.7	6.8
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
29	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	2	1.0	1.0
59	0	0.0	0.0
60	1	0.5	0.5
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	2	1.0	1.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.5	0.5
71	0	0.0	0.0
72	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
83	1	0.5	0.5
95	0	0.0	0.0
“NA”	202	96.2	96.2
Total	210	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	196	93.3	96.1
Yes	8	3.8	3.9
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	0	0.0	0.0
21	1	0.5	0.5
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
53	0	0.0	0.0
54	1	0.5	0.5
55	0	0.0	0.0
58	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	2	1.0	1.0
69	0	0.0	0.0
74	0	0.0	0.0
77	0	0.0	0.0
“NA”	206	98.1	98.1
Total	210	100.0	100.0

Arthritis
	n	%	val%
No	182	86.7	88.8
Yes	23	11.0	11.2
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
28	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	1	0.5	0.5
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	3	1.4	1.4
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	1	0.5	0.5
55	2	1.0	1.0
56	0	0.0	0.0
57	0	0.0	0.0
58	2	1.0	1.0
59	1	0.5	0.5
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	2	1.0	1.0
64	2	1.0	1.0
65	2	1.0	1.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.5	0.5
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.5	0.5
76	0	0.0	0.0
77	1	0.5	0.5
80	0	0.0	0.0
“NA”	191	91.0	91.0
Total	210	100.0	100.0

Dementia
	n	%	val%
No	200	95.2	98
Yes	4	1.9	2
Scantron_Error	0	0.0	0
NA	6	2.9	NA
Total	210	100.0	100

Dementia age
	n	%	val%
10	0	0.0	0.0
29	0	0.0	0.0
53	0	0.0	0.0
57	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	1	0.5	0.5
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
80	0	0.0	0.0
“NA”	209	99.5	99.5
Total	210	100.0	100.0

Depression
	n	%	val%
No	178	84.8	88.6
Yes	22	10.5	10.9
Scantron_Error	1	0.5	0.5
NA	9	4.3	NA
Total	210	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.5	0.5
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
28	1	0.5	0.5
29	0	0.0	0.0
30	1	0.5	0.5
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	1	0.5	0.5
44	0	0.0	0.0
45	1	0.5	0.5
46	0	0.0	0.0
47	0	0.0	0.0
48	2	1.0	1.0
49	0	0.0	0.0
50	2	1.0	1.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
6	1	0.5	0.5
60	2	1.0	1.0
61	0	0.0	0.0
62	0	0.0	0.0
63	1	0.5	0.5
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	1	0.5	0.5
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.5	0.5
72	1	0.5	0.5
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
“NA”	193	91.9	91.9
Total	210	100.0	100.0

AIDS
	n	%	val%
No	196	93.3	96.1
Yes	8	3.8	3.9
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

AIDS age
	n	%	val%
0	9	4.3	4.3
25	0	0.0	0.0
30	0	0.0	0.0
33	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	1	0.5	0.5
45	0	0.0	0.0
48	1	0.5	0.5
49	1	0.5	0.5
50	0	0.0	0.0
51	0	0.0	0.0
55	1	0.5	0.5
59	0	0.0	0.0
60	0	0.0	0.0
63	0	0.0	0.0
65	0	0.0	0.0
9	0	0.0	0.0
90	0	0.0	0.0
“NA”	197	93.8	93.8
Total	210	100.0	100.0

Other Cancer
	n	%	val%
No	179	85.2	92.7
Yes	13	6.2	6.7
Scantron_Error	1	0.5	0.5
NA	17	8.1	NA
Total	210	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	0	0.0	0.0
16	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
30	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	1	0.5	0.5
44	0	0.0	0.0
45	0	0.0	0.0
47	0	0.0	0.0
48	1	0.5	0.5
49	1	0.5	0.5
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
54	0	0.0	0.0
55	1	0.5	0.5
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.5	0.5
59	0	0.0	0.0
60	0	0.0	0.0
61	1	0.5	0.5
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.5	0.5
67	2	1.0	1.0
68	0	0.0	0.0
69	2	1.0	1.0
7	0	0.0	0.0
70	0	0.0	0.0
71	1	0.5	0.5
72	1	0.5	0.5
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
“NA”	197	93.8	93.8
Total	210	100.0	100.0

Greater CA

Heart Attack
	n	%	val%
No	277	87.9	92.6
Yes	22	7.0	7.4
Scantron_Error	0	0.0	0.0
NA	16	5.1	NA
Total	315	100.0	100.0

Heart Attack age
	n	%	val%
0	4	1.3	1.3
1	0	0.0	0.0
14	0	0.0	0.0
17	0	0.0	0.0
20	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
29	1	0.3	0.3
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	2	0.6	0.6
49	1	0.3	0.3
5	0	0.0	0.0
50	2	0.6	0.6
51	0	0.0	0.0
52	2	0.6	0.6
53	1	0.3	0.3
54	0	0.0	0.0
55	2	0.6	0.6
56	0	0.0	0.0
57	1	0.3	0.3
58	3	1.0	1.0
59	2	0.6	0.6
60	0	0.0	0.0
61	1	0.3	0.3
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	2	0.6	0.6
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	2	0.6	0.6
76	0	0.0	0.0
79	0	0.0	0.0
93	0	0.0	0.0
“NA”	289	91.7	91.7
Total	315	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	274	87.0	93.2
Yes	20	6.3	6.8
Scantron_Error	0	0.0	0.0
NA	21	6.7	NA
Total	315	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
20	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	1	0.3	0.3
30	0	0.0	0.0
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	1	0.3	0.3
5	0	0.0	0.0
50	2	0.6	0.6
51	0	0.0	0.0
52	0	0.0	0.0
53	1	0.3	0.3
54	1	0.3	0.3
55	0	0.0	0.0
56	3	1.0	1.0
57	0	0.0	0.0
58	0	0.0	0.0
59	1	0.3	0.3
60	0	0.0	0.0
61	2	0.6	0.6
62	1	0.3	0.3
63	1	0.3	0.3
64	2	0.6	0.6
65	0	0.0	0.0
66	1	0.3	0.3
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	2	0.6	0.6
71	1	0.3	0.3
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.3	0.3
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
80	0	0.0	0.0
82	0	0.0	0.0
97	0	0.0	0.0
“NA”	293	93.0	93.0
Total	315	100.0	100.0

Stroke
	n	%	val%
No	271	86.0	91.6
Yes	25	7.9	8.4
Scantron_Error	0	0.0	0.0
NA	19	6.0	NA
Total	315	100.0	100.0

Stroke age
	n	%	val%
0	5	1.6	1.6
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
30	0	0.0	0.0
34	1	0.3	0.3
35	0	0.0	0.0
38	0	0.0	0.0
40	1	0.3	0.3
42	1	0.3	0.3
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	1	0.3	0.3
51	0	0.0	0.0
52	1	0.3	0.3
53	0	0.0	0.0
54	1	0.3	0.3
55	1	0.3	0.3
56	0	0.0	0.0
57	2	0.6	0.6
58	1	0.3	0.3
59	0	0.0	0.0
60	1	0.3	0.3
61	2	0.6	0.6
62	1	0.3	0.3
63	2	0.6	0.6
64	2	0.6	0.6
65	2	0.6	0.6
66	0	0.0	0.0
67	1	0.3	0.3
68	1	0.3	0.3
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	1	0.3	0.3
74	1	0.3	0.3
75	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
“NA”	286	90.8	90.8
Total	315	100.0	100.0

Hypertension
	n	%	val%
No	89	28.3	29.6
Yes	212	67.3	70.4
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

Hypertension age
	n	%	val%
.35	1	0.3	0.3
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	2	0.6	0.6
22	2	0.6	0.6
23	0	0.0	0.0
24	0	0.0	0.0
25	1	0.3	0.3
26	0	0.0	0.0
27	1	0.3	0.3
28	1	0.3	0.3
29	2	0.6	0.6
30	5	1.6	1.6
31	0	0.0	0.0
32	1	0.3	0.3
33	2	0.6	0.6
34	0	0.0	0.0
35	5	1.6	1.6
36	4	1.3	1.3
37	0	0.0	0.0
38	1	0.3	0.3
39	1	0.3	0.3
4	0	0.0	0.0
40	12	3.8	3.8
41	1	0.3	0.3
42	0	0.0	0.0
43	1	0.3	0.3
44	1	0.3	0.3
45	16	5.1	5.1
46	3	1.0	1.0
47	2	0.6	0.6
48	3	1.0	1.0
49	3	1.0	1.0
5	0	0.0	0.0
50	28	8.9	8.9
51	0	0.0	0.0
52	1	0.3	0.3
53	2	0.6	0.6
54	10	3.2	3.2
55	30	9.5	9.5
56	3	1.0	1.0
57	3	1.0	1.0
58	4	1.3	1.3
59	3	1.0	1.0
6	1	0.3	0.3
60	11	3.5	3.5
61	6	1.9	1.9
62	6	1.9	1.9
63	2	0.6	0.6
64	2	0.6	0.6
65	2	0.6	0.6
66	3	1.0	1.0
67	0	0.0	0.0
68	3	1.0	1.0
69	2	0.6	0.6
7	0	0.0	0.0
70	1	0.3	0.3
71	1	0.3	0.3
72	1	0.3	0.3
73	1	0.3	0.3
74	0	0.0	0.0
75	1	0.3	0.3
77	0	0.0	0.0
78	1	0.3	0.3
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
89	1	0.3	0.3
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
99	2	0.6	0.6
“NA”	113	35.9	35.9
Total	315	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	266	84.4	92.7
Yes	21	6.7	7.3
Scantron_Error	0	0.0	0.0
NA	28	8.9	NA
Total	315	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	1	0.3	0.3
1	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
25	1	0.3	0.3
26	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	1	0.3	0.3
37	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	2	0.6	0.6
46	1	0.3	0.3
47	0	0.0	0.0
48	3	1.0	1.0
5	0	0.0	0.0
50	2	0.6	0.6
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	1	0.3	0.3
57	0	0.0	0.0
58	1	0.3	0.3
59	0	0.0	0.0
60	1	0.3	0.3
61	0	0.0	0.0
62	1	0.3	0.3
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.3	0.3
67	0	0.0	0.0
68	3	1.0	1.0
69	1	0.3	0.3
70	1	0.3	0.3
71	1	0.3	0.3
72	0	0.0	0.0
73	1	0.3	0.3
74	1	0.3	0.3
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
82	0	0.0	0.0
84	0	0.0	0.0
94	0	0.0	0.0
97	0	0.0	0.0
“NA”	290	92.1	92.1
Total	315	100.0	100.0

High Cholesterol
	n	%	val%
No	141	44.8	47.5
Yes	156	49.5	52.5
Scantron_Error	0	0.0	0.0
NA	18	5.7	NA
Total	315	100.0	100.0

High Cholesterol age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
12	1	0.3	0.3
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	1	0.3	0.3
27	0	0.0	0.0
28	1	0.3	0.3
29	1	0.3	0.3
30	1	0.3	0.3
31	0	0.0	0.0
32	0	0.0	0.0
33	1	0.3	0.3
34	0	0.0	0.0
35	2	0.6	0.6
36	3	1.0	1.0
37	0	0.0	0.0
38	1	0.3	0.3
39	1	0.3	0.3
4	0	0.0	0.0
40	9	2.9	2.9
41	0	0.0	0.0
42	2	0.6	0.6
43	0	0.0	0.0
44	2	0.6	0.6
45	13	4.1	4.1
46	2	0.6	0.6
47	4	1.3	1.3
48	3	1.0	1.0
49	1	0.3	0.3
5	1	0.3	0.3
50	19	6.0	6.0
51	1	0.3	0.3
52	0	0.0	0.0
53	4	1.3	1.3
54	6	1.9	1.9
55	13	4.1	4.1
56	3	1.0	1.0
57	2	0.6	0.6
58	3	1.0	1.0
59	2	0.6	0.6
6	1	0.3	0.3
60	11	3.5	3.5
61	2	0.6	0.6
62	3	1.0	1.0
63	1	0.3	0.3
64	3	1.0	1.0
65	5	1.6	1.6
66	0	0.0	0.0
67	2	0.6	0.6
68	2	0.6	0.6
69	2	0.6	0.6
7	0	0.0	0.0
70	2	0.6	0.6
71	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	1	0.3	0.3
76	0	0.0	0.0
77	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
86	0	0.0	0.0
9	0	0.0	0.0
92	1	0.3	0.3
94	0	0.0	0.0
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	0	0.0	0.0
“NA”	174	55.2	55.2
Total	315	100.0	100.0

Asthma, COPD
	n	%	val%
No	254	80.6	83
Yes	52	16.5	17
Scantron_Error	0	0.0	0
NA	9	2.9	NA
Total	315	100.0	100

Asthma, COPD age
	n	%	val%
06	2	0.6	0.6
1	0	0.0	0.0
10	0	0.0	0.0
11	0	0.0	0.0
12	1	0.3	0.3
13	0	0.0	0.0
14	1	0.3	0.3
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
23	0	0.0	0.0
25	1	0.3	0.3
26	0	0.0	0.0
27	1	0.3	0.3
28	0	0.0	0.0
29	0	0.0	0.0
3	1	0.3	0.3
30	2	0.6	0.6
31	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	6	1.9	1.9
50	5	1.6	1.6
51	0	0.0	0.0
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	3	1.0	1.0
56	1	0.3	0.3
57	0	0.0	0.0
58	2	0.6	0.6
59	0	0.0	0.0
6	2	0.6	0.6
60	2	0.6	0.6
61	0	0.0	0.0
62	1	0.3	0.3
63	1	0.3	0.3
64	2	0.6	0.6
65	1	0.3	0.3
66	0	0.0	0.0
67	1	0.3	0.3
68	0	0.0	0.0
69	1	0.3	0.3
7	5	1.6	1.6
70	1	0.3	0.3
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	1	0.3	0.3
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
81	0	0.0	0.0
9	1	0.3	0.3
93	0	0.0	0.0
97	1	0.3	0.3
“NA”	266	84.4	84.4
Total	315	100.0	100.0

Stomach ulcers
	n	%	val%
No	283	89.8	94
Yes	18	5.7	6
Scantron_Error	0	0.0	0
NA	14	4.4	NA
Total	315	100.0	100

Stomach ulcers age
	n	%	val%
0	5	1.6	1.6
10	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	1	0.3	0.3
16	0	0.0	0.0
17	0	0.0	0.0
18	1	0.3	0.3
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	1	0.3	0.3
22	0	0.0	0.0
23	0	0.0	0.0
24	1	0.3	0.3
25	1	0.3	0.3
27	0	0.0	0.0
28	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	3	1.0	1.0
36	1	0.3	0.3
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	2	0.6	0.6
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	2	0.6	0.6
66	1	0.3	0.3
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
9	0	0.0	0.0
94	0	0.0	0.0
“NA”	294	93.3	93.3
Total	315	100.0	100.0

Crohn’s Disease
	n	%	val%
No	294	93.3	97.7
Yes	7	2.2	2.3
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

Crohn’s Disease age
	n	%	val%
15	1	0.3	0.3
2	0	0.0	0.0
22	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	1	0.3	0.3
37	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
48	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.3	0.3
59	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	1	0.3	0.3
74	0	0.0	0.0
75	1	0.3	0.3
76	0	0.0	0.0
79	0	0.0	0.0
85	0	0.0	0.0
“NA”	310	98.4	98.4
Total	315	100.0	100.0

Diabetes
	n	%	val%
No	214	67.9	69.9
Yes	92	29.2	30.1
Scantron_Error	0	0.0	0.0
NA	9	2.9	NA
Total	315	100.0	100.0

Diabetes age
	n	%	val%
0	2	0.6	0.6
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	1	0.3	0.3
3	1	0.3	0.3
30	1	0.3	0.3
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	1	0.3	0.3
38	1	0.3	0.3
39	1	0.3	0.3
40	4	1.3	1.3
41	1	0.3	0.3
42	1	0.3	0.3
43	0	0.0	0.0
44	0	0.0	0.0
45	2	0.6	0.6
46	0	0.0	0.0
47	2	0.6	0.6
48	2	0.6	0.6
49	0	0.0	0.0
5	0	0.0	0.0
50	8	2.5	2.5
51	3	1.0	1.0
52	2	0.6	0.6
53	1	0.3	0.3
54	2	0.6	0.6
55	8	2.5	2.5
56	3	1.0	1.0
57	1	0.3	0.3
58	3	1.0	1.0
59	3	1.0	1.0
60	8	2.5	2.5
61	1	0.3	0.3
62	2	0.6	0.6
63	1	0.3	0.3
64	2	0.6	0.6
65	7	2.2	2.2
66	0	0.0	0.0
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
70	3	1.0	1.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	1	0.3	0.3
78	0	0.0	0.0
81	0	0.0	0.0
94	0	0.0	0.0
95	1	0.3	0.3
97	0	0.0	0.0
“NA”	233	74.0	74.0
Total	315	100.0	100.0

Kidney Problems
	n	%	val%
No	285	90.5	94.4
Yes	16	5.1	5.3
Scantron_Error	1	0.3	0.3
NA	13	4.1	NA
Total	315	100.0	100.0

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
29	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	3	1.0	1.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.3	0.3
58	0	0.0	0.0
59	1	0.3	0.3
60	1	0.3	0.3
61	0	0.0	0.0
62	1	0.3	0.3
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	2	0.6	0.6
72	0	0.0	0.0
74	1	0.3	0.3
75	0	0.0	0.0
76	0	0.0	0.0
77	1	0.3	0.3
80	0	0.0	0.0
83	0	0.0	0.0
95	0	0.0	0.0
“NA”	302	95.9	95.9
Total	315	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	294	93.3	97
Yes	9	2.9	3
Scantron_Error	0	0.0	0
NA	12	3.8	NA
Total	315	100.0	100

Cirrhosis, liver damage age
	n	%	val%
18	0	0.0	0.0
21	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	1	0.3	0.3
51	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
58	0	0.0	0.0
60	2	0.6	0.6
61	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	1	0.3	0.3
68	0	0.0	0.0
69	0	0.0	0.0
74	0	0.0	0.0
77	0	0.0	0.0
“NA”	311	98.7	98.7
Total	315	100.0	100.0

Arthritis
	n	%	val%
No	277	87.9	92.6
Yes	21	6.7	7.0
Scantron_Error	1	0.3	0.3
NA	16	5.1	NA
Total	315	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
28	0	0.0	0.0
30	1	0.3	0.3
32	1	0.3	0.3
33	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	2	0.6	0.6
41	0	0.0	0.0
42	1	0.3	0.3
43	0	0.0	0.0
44	0	0.0	0.0
45	4	1.3	1.3
46	0	0.0	0.0
47	0	0.0	0.0
48	2	0.6	0.6
49	0	0.0	0.0
50	1	0.3	0.3
51	1	0.3	0.3
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	1	0.3	0.3
57	1	0.3	0.3
58	0	0.0	0.0
59	0	0.0	0.0
60	3	1.0	1.0
61	0	0.0	0.0
62	1	0.3	0.3
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
“NA”	294	93.3	93.3
Total	315	100.0	100.0

Dementia
	n	%	val%
No	301	95.6	99
Yes	3	1.0	1
Scantron_Error	0	0.0	0
NA	11	3.5	NA
Total	315	100.0	100

Dementia age
	n	%	val%
10	0	0.0	0.0
29	1	0.3	0.3
53	0	0.0	0.0
57	1	0.3	0.3
60	1	0.3	0.3
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
80	0	0.0	0.0
“NA”	311	98.7	98.7
Total	315	100.0	100.0

Depression
	n	%	val%
No	271	86.0	89.7
Yes	31	9.8	10.3
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	1	0.3	0.3
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	2	0.6	0.6
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	1	0.3	0.3
32	0	0.0	0.0
33	0	0.0	0.0
34	1	0.3	0.3
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	1	0.3	0.3
39	0	0.0	0.0
4	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	2	0.6	0.6
43	0	0.0	0.0
44	1	0.3	0.3
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	1	0.3	0.3
50	0	0.0	0.0
51	0	0.0	0.0
52	1	0.3	0.3
53	1	0.3	0.3
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.3	0.3
58	0	0.0	0.0
59	0	0.0	0.0
6	0	0.0	0.0
60	1	0.3	0.3
61	1	0.3	0.3
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.3	0.3
66	2	0.6	0.6
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	3	1.0	1.0
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.3	0.3
75	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
“NA”	290	92.1	92.1
Total	315	100.0	100.0

AIDS
	n	%	val%
No	300	95.2	98.7
Yes	4	1.3	1.3
Scantron_Error	0	0.0	0.0
NA	11	3.5	NA
Total	315	100.0	100.0

AIDS age
	n	%	val%
0	5	1.6	1.6
25	0	0.0	0.0
30	1	0.3	0.3
33	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
45	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
55	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
63	1	0.3	0.3
65	0	0.0	0.0
9	1	0.3	0.3
90	0	0.0	0.0
“NA”	307	97.5	97.5
Total	315	100.0	100.0

Other Cancer
	n	%	val%
No	277	87.9	93.6
Yes	19	6.0	6.4
Scantron_Error	0	0.0	0.0
NA	19	6.0	NA
Total	315	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	0	0.0	0.0
16	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
30	0	0.0	0.0
35	0	0.0	0.0
36	1	0.3	0.3
38	0	0.0	0.0
39	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	4	1.3	1.3
51	0	0.0	0.0
52	0	0.0	0.0
54	2	0.6	0.6
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	0.3	0.3
61	2	0.6	0.6
62	0	0.0	0.0
63	3	1.0	1.0
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	1	0.3	0.3
68	0	0.0	0.0
69	1	0.3	0.3
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
“NA”	298	94.6	94.6
Total	315	100.0	100.0

Detroit

Heart Attack
	n	%	val%
No	313	87.9	91.5
Yes	29	8.1	8.5
Scantron_Error	0	0.0	0.0
NA	14	3.9	NA
Total	356	100.0	100.0

Heart Attack age
	n	%	val%
0	8	2.2	2.2
1	0	0.0	0.0
14	0	0.0	0.0
17	1	0.3	0.3
20	1	0.3	0.3
24	0	0.0	0.0
25	0	0.0	0.0
26	1	0.3	0.3
27	0	0.0	0.0
29	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
44	0	0.0	0.0
45	3	0.8	0.8
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
5	0	0.0	0.0
50	1	0.3	0.3
51	1	0.3	0.3
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	1	0.3	0.3
57	0	0.0	0.0
58	2	0.6	0.6
59	1	0.3	0.3
60	3	0.8	0.8
61	1	0.3	0.3
62	0	0.0	0.0
63	2	0.6	0.6
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	3	0.8	0.8
68	0	0.0	0.0
69	0	0.0	0.0
70	1	0.3	0.3
71	0	0.0	0.0
72	0	0.0	0.0
73	1	0.3	0.3
74	0	0.0	0.0
76	1	0.3	0.3
79	1	0.3	0.3
93	0	0.0	0.0
“NA”	319	89.6	89.6
Total	356	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	317	89.0	93.5
Yes	22	6.2	6.5
Scantron_Error	0	0.0	0.0
NA	17	4.8	NA
Total	356	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	1	0.3	0.3
18	1	0.3	0.3
20	1	0.3	0.3
25	0	0.0	0.0
27	0	0.0	0.0
28	1	0.3	0.3
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	1	0.3	0.3
48	1	0.3	0.3
49	0	0.0	0.0
5	0	0.0	0.0
50	1	0.3	0.3
51	1	0.3	0.3
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	2	0.6	0.6
57	1	0.3	0.3
58	1	0.3	0.3
59	0	0.0	0.0
60	2	0.6	0.6
61	0	0.0	0.0
62	3	0.8	0.8
63	1	0.3	0.3
64	0	0.0	0.0
65	0	0.0	0.0
66	1	0.3	0.3
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	1	0.3	0.3
72	1	0.3	0.3
73	1	0.3	0.3
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
80	0	0.0	0.0
82	0	0.0	0.0
97	0	0.0	0.0
“NA”	334	93.8	93.8
Total	356	100.0	100.0

Stroke
	n	%	val%
No	303	85.1	89.1
Yes	37	10.4	10.9
Scantron_Error	0	0.0	0.0
NA	16	4.5	NA
Total	356	100.0	100.0

Stroke age
	n	%	val%
0	11	3.1	3.1
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
23	0	0.0	0.0
25	1	0.3	0.3
27	1	0.3	0.3
30	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	2	0.6	0.6
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	1	0.3	0.3
48	3	0.8	0.8
49	0	0.0	0.0
5	1	0.3	0.3
50	1	0.3	0.3
51	0	0.0	0.0
52	2	0.6	0.6
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.3	0.3
58	1	0.3	0.3
59	4	1.1	1.1
60	0	0.0	0.0
61	0	0.0	0.0
62	3	0.8	0.8
63	2	0.6	0.6
64	1	0.3	0.3
65	2	0.6	0.6
66	1	0.3	0.3
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
70	1	0.3	0.3
71	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
78	0	0.0	0.0
79	2	0.6	0.6
8	0	0.0	0.0
“NA”	312	87.6	87.6
Total	356	100.0	100.0

Hypertension
	n	%	val%
No	101	28.4	30
Yes	236	66.3	70
Scantron_Error	0	0.0	0
NA	19	5.3	NA
Total	356	100.0	100

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	1	0.3	0.3
17	1	0.3	0.3
18	1	0.3	0.3
19	0	0.0	0.0
2	0	0.0	0.0
20	2	0.6	0.6
21	1	0.3	0.3
22	1	0.3	0.3
23	1	0.3	0.3
24	3	0.8	0.8
25	0	0.0	0.0
26	2	0.6	0.6
27	0	0.0	0.0
28	1	0.3	0.3
29	1	0.3	0.3
30	7	2.0	2.0
31	1	0.3	0.3
32	1	0.3	0.3
33	1	0.3	0.3
34	0	0.0	0.0
35	10	2.8	2.8
36	0	0.0	0.0
37	2	0.6	0.6
38	1	0.3	0.3
39	1	0.3	0.3
4	1	0.3	0.3
40	19	5.3	5.3
41	1	0.3	0.3
42	3	0.8	0.8
43	3	0.8	0.8
44	1	0.3	0.3
45	14	3.9	3.9
46	1	0.3	0.3
47	3	0.8	0.8
48	3	0.8	0.8
49	4	1.1	1.1
5	2	0.6	0.6
50	28	7.9	7.9
51	2	0.6	0.6
52	3	0.8	0.8
53	2	0.6	0.6
54	7	2.0	2.0
55	13	3.7	3.7
56	4	1.1	1.1
57	3	0.8	0.8
58	6	1.7	1.7
59	3	0.8	0.8
6	0	0.0	0.0
60	16	4.5	4.5
61	2	0.6	0.6
62	4	1.1	1.1
63	5	1.4	1.4
64	3	0.8	0.8
65	2	0.6	0.6
66	0	0.0	0.0
67	3	0.8	0.8
68	2	0.6	0.6
69	3	0.8	0.8
7	0	0.0	0.0
70	2	0.6	0.6
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	2	0.6	0.6
75	1	0.3	0.3
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	2	0.6	0.6
89	0	0.0	0.0
9	1	0.3	0.3
92	0	0.0	0.0
94	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
99	0	0.0	0.0
“NA”	143	40.2	40.2
Total	356	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	298	83.7	91.1
Yes	29	8.1	8.9
Scantron_Error	0	0.0	0.0
NA	29	8.1	NA
Total	356	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	3	0.8	0.8
1	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
30	1	0.3	0.3
31	1	0.3	0.3
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	2	0.6	0.6
5	0	0.0	0.0
50	1	0.3	0.3
51	1	0.3	0.3
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	1	0.3	0.3
57	2	0.6	0.6
58	2	0.6	0.6
59	0	0.0	0.0
60	3	0.8	0.8
61	0	0.0	0.0
62	1	0.3	0.3
63	0	0.0	0.0
64	1	0.3	0.3
65	1	0.3	0.3
66	2	0.6	0.6
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.3	0.3
70	3	0.8	0.8
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	1	0.3	0.3
77	0	0.0	0.0
78	0	0.0	0.0
82	1	0.3	0.3
84	0	0.0	0.0
94	0	0.0	0.0
97	0	0.0	0.0
“NA”	327	91.9	91.9
Total	356	100.0	100.0

High Cholesterol
	n	%	val%
No	160	44.9	46.9
Yes	181	50.8	53.1
Scantron_Error	0	0.0	0.0
NA	15	4.2	NA
Total	356	100.0	100.0

High Cholesterol age
	n	%	val%
1	1	0.3	0.3
10	1	0.3	0.3
12	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	2	0.6	0.6
21	0	0.0	0.0
22	0	0.0	0.0
24	1	0.3	0.3
25	0	0.0	0.0
26	1	0.3	0.3
27	0	0.0	0.0
28	1	0.3	0.3
29	1	0.3	0.3
30	3	0.8	0.8
31	0	0.0	0.0
32	1	0.3	0.3
33	0	0.0	0.0
34	1	0.3	0.3
35	4	1.1	1.1
36	0	0.0	0.0
37	1	0.3	0.3
38	1	0.3	0.3
39	0	0.0	0.0
4	0	0.0	0.0
40	6	1.7	1.7
41	0	0.0	0.0
42	2	0.6	0.6
43	0	0.0	0.0
44	0	0.0	0.0
45	6	1.7	1.7
46	1	0.3	0.3
47	1	0.3	0.3
48	2	0.6	0.6
49	2	0.6	0.6
5	0	0.0	0.0
50	15	4.2	4.2
51	1	0.3	0.3
52	3	0.8	0.8
53	2	0.6	0.6
54	2	0.6	0.6
55	11	3.1	3.1
56	6	1.7	1.7
57	5	1.4	1.4
58	3	0.8	0.8
59	5	1.4	1.4
6	1	0.3	0.3
60	13	3.7	3.7
61	1	0.3	0.3
62	4	1.1	1.1
63	7	2.0	2.0
64	2	0.6	0.6
65	7	2.0	2.0
66	3	0.8	0.8
67	0	0.0	0.0
68	2	0.6	0.6
69	0	0.0	0.0
7	0	0.0	0.0
70	3	0.8	0.8
71	0	0.0	0.0
72	0	0.0	0.0
73	1	0.3	0.3
74	3	0.8	0.8
75	1	0.3	0.3
76	0	0.0	0.0
77	0	0.0	0.0
8	0	0.0	0.0
80	1	0.3	0.3
86	1	0.3	0.3
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	1	0.3	0.3
“NA”	212	59.6	59.6
Total	356	100.0	100.0

Asthma, COPD
	n	%	val%
No	283	79.5	80.4
Yes	68	19.1	19.3
Scantron_Error	1	0.3	0.3
NA	4	1.1	NA
Total	356	100.0	100.0

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	3	0.8	0.8
10	3	0.8	0.8
11	1	0.3	0.3
12	0	0.0	0.0
13	0	0.0	0.0
14	2	0.6	0.6
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	1	0.3	0.3
19	2	0.6	0.6
2	0	0.0	0.0
20	1	0.3	0.3
21	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
26	1	0.3	0.3
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	1	0.3	0.3
33	0	0.0	0.0
35	1	0.3	0.3
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
5	4	1.1	1.1
50	2	0.6	0.6
51	1	0.3	0.3
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	6	1.7	1.7
57	4	1.1	1.1
58	2	0.6	0.6
59	1	0.3	0.3
6	0	0.0	0.0
60	4	1.1	1.1
61	0	0.0	0.0
62	2	0.6	0.6
63	1	0.3	0.3
64	2	0.6	0.6
65	1	0.3	0.3
66	2	0.6	0.6
67	1	0.3	0.3
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.3	0.3
71	1	0.3	0.3
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.3	0.3
75	1	0.3	0.3
77	0	0.0	0.0
78	1	0.3	0.3
79	1	0.3	0.3
8	0	0.0	0.0
80	0	0.0	0.0
81	1	0.3	0.3
9	1	0.3	0.3
93	0	0.0	0.0
97	0	0.0	0.0
“NA”	295	82.9	82.9
Total	356	100.0	100.0

Stomach ulcers
	n	%	val%
No	321	90.2	90.9
Yes	32	9.0	9.1
Scantron_Error	0	0.0	0.0
NA	3	0.8	NA
Total	356	100.0	100.0

Stomach ulcers age
	n	%	val%
0	10	2.8	2.8
10	0	0.0	0.0
13	0	0.0	0.0
14	1	0.3	0.3
15	0	0.0	0.0
16	0	0.0	0.0
17	1	0.3	0.3
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	1	0.3	0.3
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
3	0	0.0	0.0
30	1	0.3	0.3
32	0	0.0	0.0
34	1	0.3	0.3
35	1	0.3	0.3
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
44	0	0.0	0.0
45	1	0.3	0.3
46	1	0.3	0.3
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
50	2	0.6	0.6
52	0	0.0	0.0
53	0	0.0	0.0
54	1	0.3	0.3
55	0	0.0	0.0
56	0	0.0	0.0
57	1	0.3	0.3
58	0	0.0	0.0
59	0	0.0	0.0
60	2	0.6	0.6
61	0	0.0	0.0
62	1	0.3	0.3
63	1	0.3	0.3
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	1	0.3	0.3
9	0	0.0	0.0
94	0	0.0	0.0
“NA”	324	91.0	91.0
Total	356	100.0	100.0

Crohn’s Disease
	n	%	val%
No	339	95.2	98
Yes	7	2.0	2
Scantron_Error	0	0.0	0
NA	10	2.8	NA
Total	356	100.0	100

Crohn’s Disease age
	n	%	val%
15	0	0.0	0.0
2	0	0.0	0.0
22	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	1	0.3	0.3
35	0	0.0	0.0
37	0	0.0	0.0
39	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
44	1	0.3	0.3
45	0	0.0	0.0
46	0	0.0	0.0
48	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	0.3	0.3
62	0	0.0	0.0
63	0	0.0	0.0
64	1	0.3	0.3
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
79	0	0.0	0.0
85	0	0.0	0.0
“NA”	348	97.8	97.8
Total	356	100.0	100.0

Diabetes
	n	%	val%
No	241	67.7	68.3
Yes	112	31.5	31.7
Scantron_Error	0	0.0	0.0
NA	3	0.8	NA
Total	356	100.0	100.0

Diabetes age
	n	%	val%
0	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	1	0.3	0.3
22	1	0.3	0.3
24	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	3	0.8	0.8
3	0	0.0	0.0
30	3	0.8	0.8
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	4	1.1	1.1
36	1	0.3	0.3
37	0	0.0	0.0
38	1	0.3	0.3
39	1	0.3	0.3
40	2	0.6	0.6
41	0	0.0	0.0
42	3	0.8	0.8
43	2	0.6	0.6
44	0	0.0	0.0
45	3	0.8	0.8
46	0	0.0	0.0
47	2	0.6	0.6
48	1	0.3	0.3
49	1	0.3	0.3
5	0	0.0	0.0
50	9	2.5	2.5
51	0	0.0	0.0
52	1	0.3	0.3
53	1	0.3	0.3
54	2	0.6	0.6
55	7	2.0	2.0
56	2	0.6	0.6
57	6	1.7	1.7
58	3	0.8	0.8
59	1	0.3	0.3
60	8	2.2	2.2
61	1	0.3	0.3
62	4	1.1	1.1
63	3	0.8	0.8
64	1	0.3	0.3
65	4	1.1	1.1
66	1	0.3	0.3
67	1	0.3	0.3
68	2	0.6	0.6
69	3	0.8	0.8
70	2	0.6	0.6
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
78	0	0.0	0.0
81	1	0.3	0.3
94	0	0.0	0.0
95	0	0.0	0.0
97	0	0.0	0.0
“NA”	263	73.9	73.9
Total	356	100.0	100.0

Kidney Problems
	n	%	val%
No	333	93.5	94.3
Yes	20	5.6	5.7
Scantron_Error	0	0.0	0.0
NA	3	0.8	NA
Total	356	100.0	100.0

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	2	0.6	0.6
16	0	0.0	0.0
17	0	0.0	0.0
29	1	0.3	0.3
35	0	0.0	0.0
38	0	0.0	0.0
40	1	0.3	0.3
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.3	0.3
49	0	0.0	0.0
5	0	0.0	0.0
50	2	0.6	0.6
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.3	0.3
56	0	0.0	0.0
57	2	0.6	0.6
58	0	0.0	0.0
59	2	0.6	0.6
60	1	0.3	0.3
61	1	0.3	0.3
62	1	0.3	0.3
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	1	0.3	0.3
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
83	0	0.0	0.0
95	0	0.0	0.0
“NA”	338	94.9	94.9
Total	356	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	345	96.9	97.7
Yes	8	2.2	2.3
Scantron_Error	0	0.0	0.0
NA	3	0.8	NA
Total	356	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	1	0.3	0.3
21	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
45	1	0.3	0.3
47	0	0.0	0.0
48	1	0.3	0.3
49	1	0.3	0.3
50	0	0.0	0.0
51	1	0.3	0.3
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
58	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	2	0.6	0.6
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
74	1	0.3	0.3
77	0	0.0	0.0
“NA”	348	97.8	97.8
Total	356	100.0	100.0

Arthritis
	n	%	val%
No	298	83.7	84.9
Yes	53	14.9	15.1
Scantron_Error	0	0.0	0.0
NA	5	1.4	NA
Total	356	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	0	0.0	0.0
19	1	0.3	0.3
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	1	0.3	0.3
24	0	0.0	0.0
25	0	0.0	0.0
28	0	0.0	0.0
30	2	0.6	0.6
32	0	0.0	0.0
33	0	0.0	0.0
35	1	0.3	0.3
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	1	0.3	0.3
40	3	0.8	0.8
41	1	0.3	0.3
42	1	0.3	0.3
43	0	0.0	0.0
44	3	0.8	0.8
45	1	0.3	0.3
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	4	1.1	1.1
51	1	0.3	0.3
52	1	0.3	0.3
53	1	0.3	0.3
54	3	0.8	0.8
55	5	1.4	1.4
56	3	0.8	0.8
57	2	0.6	0.6
58	1	0.3	0.3
59	0	0.0	0.0
60	5	1.4	1.4
61	1	0.3	0.3
62	0	0.0	0.0
63	1	0.3	0.3
64	0	0.0	0.0
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	1	0.3	0.3
71	0	0.0	0.0
72	1	0.3	0.3
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	1	0.3	0.3
“NA”	309	86.8	86.8
Total	356	100.0	100.0

Dementia
	n	%	val%
No	350	98.3	99.4
Yes	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	4	1.1	NA
Total	356	100.0	100.0

Dementia age
	n	%	val%
10	0	0.0	0.0
29	0	0.0	0.0
53	1	0.3	0.3
57	0	0.0	0.0
60	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
80	0	0.0	0.0
“NA”	355	99.7	99.7
Total	356	100.0	100.0

Depression
	n	%	val%
No	296	83.1	84.3
Yes	55	15.4	15.7
Scantron_Error	0	0.0	0.0
NA	5	1.4	NA
Total	356	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	0	0.0	0.0
16	1	0.3	0.3
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.3	0.3
20	3	0.8	0.8
21	0	0.0	0.0
22	0	0.0	0.0
24	1	0.3	0.3
25	1	0.3	0.3
26	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	1	0.3	0.3
34	0	0.0	0.0
35	1	0.3	0.3
36	1	0.3	0.3
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	1	0.3	0.3
41	0	0.0	0.0
42	1	0.3	0.3
43	1	0.3	0.3
44	1	0.3	0.3
45	2	0.6	0.6
46	0	0.0	0.0
47	1	0.3	0.3
48	0	0.0	0.0
49	0	0.0	0.0
50	4	1.1	1.1
51	0	0.0	0.0
52	1	0.3	0.3
53	1	0.3	0.3
54	2	0.6	0.6
55	2	0.6	0.6
56	1	0.3	0.3
57	2	0.6	0.6
58	2	0.6	0.6
59	1	0.3	0.3
6	0	0.0	0.0
60	2	0.6	0.6
61	2	0.6	0.6
62	0	0.0	0.0
63	1	0.3	0.3
64	1	0.3	0.3
65	1	0.3	0.3
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.3	0.3
75	1	0.3	0.3
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	1	0.3	0.3
“NA”	313	87.9	87.9
Total	356	100.0	100.0

AIDS
	n	%	val%
No	343	96.3	99.1
Yes	3	0.8	0.9
Scantron_Error	0	0.0	0.0
NA	10	2.8	NA
Total	356	100.0	100.0

AIDS age
	n	%	val%
0	10	2.8	2.8
25	0	0.0	0.0
30	0	0.0	0.0
33	1	0.3	0.3
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
45	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	1	0.3	0.3
51	0	0.0	0.0
55	0	0.0	0.0
59	1	0.3	0.3
60	0	0.0	0.0
63	0	0.0	0.0
65	0	0.0	0.0
9	0	0.0	0.0
90	0	0.0	0.0
“NA”	343	96.3	96.3
Total	356	100.0	100.0

Other Cancer
	n	%	val%
No	319	89.6	93.8
Yes	21	5.9	6.2
Scantron_Error	0	0.0	0.0
NA	16	4.5	NA
Total	356	100.0	100.0

Other Cancer age
	n	%	val%
10	1	0.3	0.3
12	0	0.0	0.0
16	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	1	0.3	0.3
24	0	0.0	0.0
30	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	2	0.6	0.6
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	1	0.3	0.3
51	2	0.6	0.6
52	0	0.0	0.0
54	1	0.3	0.3
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.3	0.3
59	2	0.6	0.6
60	0	0.0	0.0
61	0	0.0	0.0
62	1	0.3	0.3
63	2	0.6	0.6
64	0	0.0	0.0
65	1	0.3	0.3
66	3	0.8	0.8
67	0	0.0	0.0
68	1	0.3	0.3
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.3	0.3
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
“NA”	336	94.4	94.4
Total	356	100.0	100.0

Louisiana

Heart Attack
	n	%	val%
No	494	84.4	91.8
Yes	44	7.5	8.2
Scantron_Error	0	0.0	0.0
NA	47	8.0	NA
Total	585	100.0	100.0

Heart Attack age
	n	%	val%
0	1	0.2	0.2
1	0	0.0	0.0
14	1	0.2	0.2
17	0	0.0	0.0
20	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
29	0	0.0	0.0
31	0	0.0	0.0
32	1	0.2	0.2
34	0	0.0	0.0
35	0	0.0	0.0
38	1	0.2	0.2
40	0	0.0	0.0
42	0	0.0	0.0
44	2	0.3	0.3
45	0	0.0	0.0
46	1	0.2	0.2
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	2	0.3	0.3
51	4	0.7	0.7
52	1	0.2	0.2
53	0	0.0	0.0
54	2	0.3	0.3
55	1	0.2	0.2
56	2	0.3	0.3
57	1	0.2	0.2
58	0	0.0	0.0
59	3	0.5	0.5
60	2	0.3	0.3
61	0	0.0	0.0
62	1	0.2	0.2
63	0	0.0	0.0
64	3	0.5	0.5
65	2	0.3	0.3
66	1	0.2	0.2
67	0	0.0	0.0
68	0	0.0	0.0
69	2	0.3	0.3
70	3	0.5	0.5
71	0	0.0	0.0
72	1	0.2	0.2
73	0	0.0	0.0
74	1	0.2	0.2
76	1	0.2	0.2
79	0	0.0	0.0
93	1	0.2	0.2
“NA”	544	93.0	93.0
Total	585	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	496	84.8	93.4
Yes	35	6.0	6.6
Scantron_Error	0	0.0	0.0
NA	54	9.2	NA
Total	585	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
20	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	2	0.3	0.3
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	1	0.2	0.2
51	1	0.2	0.2
52	3	0.5	0.5
53	1	0.2	0.2
54	1	0.2	0.2
55	3	0.5	0.5
56	3	0.5	0.5
57	0	0.0	0.0
58	0	0.0	0.0
59	4	0.7	0.7
60	1	0.2	0.2
61	1	0.2	0.2
62	1	0.2	0.2
63	0	0.0	0.0
64	1	0.2	0.2
65	1	0.2	0.2
66	1	0.2	0.2
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	1	0.2	0.2
70	2	0.3	0.3
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
80	1	0.2	0.2
82	0	0.0	0.0
97	1	0.2	0.2
“NA”	555	94.9	94.9
Total	585	100.0	100.0

Stroke
	n	%	val%
No	474	81.0	89.1
Yes	58	9.9	10.9
Scantron_Error	0	0.0	0.0
NA	53	9.1	NA
Total	585	100.0	100.0

Stroke age
	n	%	val%
0	6	1.0	1.0
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	1	0.2	0.2
23	1	0.2	0.2
25	0	0.0	0.0
27	0	0.0	0.0
30	1	0.2	0.2
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	2	0.3	0.3
42	0	0.0	0.0
43	0	0.0	0.0
45	2	0.3	0.3
46	1	0.2	0.2
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	1	0.2	0.2
50	1	0.2	0.2
51	2	0.3	0.3
52	2	0.3	0.3
53	1	0.2	0.2
54	6	1.0	1.0
55	1	0.2	0.2
56	3	0.5	0.5
57	4	0.7	0.7
58	1	0.2	0.2
59	1	0.2	0.2
60	7	1.2	1.2
61	1	0.2	0.2
62	2	0.3	0.3
63	3	0.5	0.5
64	1	0.2	0.2
65	2	0.3	0.3
66	1	0.2	0.2
67	0	0.0	0.0
68	2	0.3	0.3
69	1	0.2	0.2
70	3	0.5	0.5
71	1	0.2	0.2
72	1	0.2	0.2
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
“NA”	523	89.4	89.4
Total	585	100.0	100.0

Hypertension
	n	%	val%
No	130	22.2	23.4
Yes	420	71.8	75.7
Scantron_Error	5	0.9	0.9
NA	30	5.1	NA
Total	585	100.0	100.0

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	0	0.0	0.0
10	0	0.0	0.0
12	2	0.3	0.3
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	1	0.2	0.2
17	0	0.0	0.0
18	1	0.2	0.2
19	1	0.2	0.2
2	0	0.0	0.0
20	3	0.5	0.5
21	0	0.0	0.0
22	0	0.0	0.0
23	1	0.2	0.2
24	1	0.2	0.2
25	3	0.5	0.5
26	0	0.0	0.0
27	0	0.0	0.0
28	2	0.3	0.3
29	0	0.0	0.0
30	6	1.0	1.0
31	2	0.3	0.3
32	1	0.2	0.2
33	2	0.3	0.3
34	1	0.2	0.2
35	6	1.0	1.0
36	4	0.7	0.7
37	1	0.2	0.2
38	5	0.9	0.9
39	3	0.5	0.5
4	0	0.0	0.0
40	20	3.4	3.4
41	2	0.3	0.3
42	5	0.9	0.9
43	3	0.5	0.5
44	4	0.7	0.7
45	23	3.9	3.9
46	6	1.0	1.0
47	2	0.3	0.3
48	10	1.7	1.7
49	5	0.9	0.9
5	2	0.3	0.3
50	46	7.9	7.9
51	7	1.2	1.2
52	4	0.7	0.7
53	2	0.3	0.3
54	7	1.2	1.2
55	17	2.9	2.9
56	16	2.7	2.7
57	9	1.5	1.5
58	12	2.1	2.1
59	9	1.5	1.5
6	0	0.0	0.0
60	35	6.0	6.0
61	4	0.7	0.7
62	10	1.7	1.7
63	3	0.5	0.5
64	6	1.0	1.0
65	11	1.9	1.9
66	4	0.7	0.7
67	4	0.7	0.7
68	3	0.5	0.5
69	1	0.2	0.2
7	1	0.2	0.2
70	8	1.4	1.4
71	3	0.5	0.5
72	3	0.5	0.5
73	2	0.3	0.3
74	1	0.2	0.2
75	1	0.2	0.2
77	0	0.0	0.0
78	0	0.0	0.0
79	1	0.2	0.2
8	1	0.2	0.2
80	0	0.0	0.0
89	0	0.0	0.0
9	1	0.2	0.2
92	0	0.0	0.0
94	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
99	1	0.2	0.2
“NA”	224	38.3	38.3
Total	585	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	475	81.2	91.3
Yes	45	7.7	8.7
Scantron_Error	0	0.0	0.0
NA	65	11.1	NA
Total	585	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	3	0.5	0.5
1	0	0.0	0.0
16	0	0.0	0.0
17	1	0.2	0.2
19	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
40	3	0.5	0.5
41	1	0.2	0.2
42	0	0.0	0.0
44	2	0.3	0.3
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
5	1	0.2	0.2
50	5	0.9	0.9
51	1	0.2	0.2
52	0	0.0	0.0
53	2	0.3	0.3
54	2	0.3	0.3
55	3	0.5	0.5
56	2	0.3	0.3
57	3	0.5	0.5
58	1	0.2	0.2
59	2	0.3	0.3
60	2	0.3	0.3
61	1	0.2	0.2
62	1	0.2	0.2
63	1	0.2	0.2
64	4	0.7	0.7
65	1	0.2	0.2
66	0	0.0	0.0
67	0	0.0	0.0
68	1	0.2	0.2
69	1	0.2	0.2
70	3	0.5	0.5
71	0	0.0	0.0
72	1	0.2	0.2
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
77	1	0.2	0.2
78	0	0.0	0.0
82	0	0.0	0.0
84	0	0.0	0.0
94	1	0.2	0.2
97	0	0.0	0.0
“NA”	535	91.5	91.5
Total	585	100.0	100.0

High Cholesterol
	n	%	val%
No	220	37.6	40.6
Yes	321	54.9	59.2
Scantron_Error	1	0.2	0.2
NA	43	7.4	NA
Total	585	100.0	100.0

High Cholesterol age
	n	%	val%
1	0	0.0	0.0
10	2	0.3	0.3
12	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	1	0.2	0.2
18	0	0.0	0.0
19	1	0.2	0.2
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	1	0.2	0.2
25	1	0.2	0.2
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	1	0.2	0.2
33	0	0.0	0.0
34	2	0.3	0.3
35	5	0.9	0.9
36	3	0.5	0.5
37	1	0.2	0.2
38	3	0.5	0.5
39	2	0.3	0.3
4	0	0.0	0.0
40	5	0.9	0.9
41	2	0.3	0.3
42	2	0.3	0.3
43	2	0.3	0.3
44	4	0.7	0.7
45	12	2.1	2.1
46	3	0.5	0.5
47	1	0.2	0.2
48	6	1.0	1.0
49	2	0.3	0.3
5	2	0.3	0.3
50	24	4.1	4.1
51	7	1.2	1.2
52	6	1.0	1.0
53	4	0.7	0.7
54	8	1.4	1.4
55	21	3.6	3.6
56	9	1.5	1.5
57	7	1.2	1.2
58	5	0.9	0.9
59	6	1.0	1.0
6	0	0.0	0.0
60	24	4.1	4.1
61	7	1.2	1.2
62	11	1.9	1.9
63	4	0.7	0.7
64	5	0.9	0.9
65	11	1.9	1.9
66	2	0.3	0.3
67	2	0.3	0.3
68	7	1.2	1.2
69	3	0.5	0.5
7	1	0.2	0.2
70	8	1.4	1.4
71	3	0.5	0.5
72	1	0.2	0.2
73	2	0.3	0.3
74	1	0.2	0.2
75	3	0.5	0.5
76	0	0.0	0.0
77	1	0.2	0.2
8	0	0.0	0.0
80	0	0.0	0.0
86	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	1	0.2	0.2
“NA”	327	55.9	55.9
Total	585	100.0	100.0

Asthma, COPD
	n	%	val%
No	472	80.7	83.5
Yes	92	15.7	16.3
Scantron_Error	1	0.2	0.2
NA	20	3.4	NA
Total	585	100.0	100.0

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	2	0.3	0.3
10	2	0.3	0.3
11	2	0.3	0.3
12	3	0.5	0.5
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	2	0.3	0.3
18	0	0.0	0.0
19	0	0.0	0.0
2	2	0.3	0.3
20	3	0.5	0.5
21	1	0.2	0.2
23	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	1	0.2	0.2
30	1	0.2	0.2
31	1	0.2	0.2
32	0	0.0	0.0
33	0	0.0	0.0
35	1	0.2	0.2
37	0	0.0	0.0
38	0	0.0	0.0
39	1	0.2	0.2
4	2	0.3	0.3
40	1	0.2	0.2
42	1	0.2	0.2
43	0	0.0	0.0
45	1	0.2	0.2
46	0	0.0	0.0
47	0	0.0	0.0
48	1	0.2	0.2
49	0	0.0	0.0
5	0	0.0	0.0
50	6	1.0	1.0
51	2	0.3	0.3
52	0	0.0	0.0
53	1	0.2	0.2
54	1	0.2	0.2
55	2	0.3	0.3
56	2	0.3	0.3
57	2	0.3	0.3
58	1	0.2	0.2
59	1	0.2	0.2
6	2	0.3	0.3
60	7	1.2	1.2
61	0	0.0	0.0
62	2	0.3	0.3
63	4	0.7	0.7
64	0	0.0	0.0
65	2	0.3	0.3
66	2	0.3	0.3
67	1	0.2	0.2
68	1	0.2	0.2
69	1	0.2	0.2
7	2	0.3	0.3
70	3	0.5	0.5
71	1	0.2	0.2
72	0	0.0	0.0
73	1	0.2	0.2
74	1	0.2	0.2
75	2	0.3	0.3
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	2	0.3	0.3
80	1	0.2	0.2
81	0	0.0	0.0
9	0	0.0	0.0
93	1	0.2	0.2
97	0	0.0	0.0
“NA”	503	86.0	86.0
Total	585	100.0	100.0

Stomach ulcers
	n	%	val%
No	503	86.0	90.3
Yes	53	9.1	9.5
Scantron_Error	1	0.2	0.2
NA	28	4.8	NA
Total	585	100.0	100.0

Stomach ulcers age
	n	%	val%
0	2	0.3	0.3
10	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	1	0.2	0.2
19	0	0.0	0.0
2	0	0.0	0.0
20	1	0.2	0.2
21	0	0.0	0.0
22	2	0.3	0.3
23	0	0.0	0.0
24	1	0.2	0.2
25	2	0.3	0.3
27	1	0.2	0.2
28	0	0.0	0.0
3	0	0.0	0.0
30	5	0.9	0.9
32	0	0.0	0.0
34	0	0.0	0.0
35	4	0.7	0.7
36	1	0.2	0.2
38	0	0.0	0.0
39	1	0.2	0.2
40	0	0.0	0.0
42	2	0.3	0.3
44	0	0.0	0.0
45	2	0.3	0.3
46	1	0.2	0.2
47	0	0.0	0.0
48	1	0.2	0.2
49	0	0.0	0.0
50	4	0.7	0.7
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.2	0.2
56	0	0.0	0.0
57	1	0.2	0.2
58	1	0.2	0.2
59	1	0.2	0.2
60	5	0.9	0.9
61	0	0.0	0.0
62	1	0.2	0.2
63	1	0.2	0.2
64	1	0.2	0.2
65	1	0.2	0.2
66	1	0.2	0.2
67	1	0.2	0.2
68	1	0.2	0.2
69	0	0.0	0.0
7	1	0.2	0.2
70	1	0.2	0.2
71	0	0.0	0.0
72	0	0.0	0.0
73	1	0.2	0.2
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
9	1	0.2	0.2
94	0	0.0	0.0
“NA”	534	91.3	91.3
Total	585	100.0	100.0

Crohn’s Disease
	n	%	val%
No	542	92.6	97
Yes	17	2.9	3
Scantron_Error	0	0.0	0
NA	26	4.4	NA
Total	585	100.0	100

Crohn’s Disease age
	n	%	val%
15	0	0.0	0.0
2	1	0.2	0.2
22	1	0.2	0.2
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
37	0	0.0	0.0
39	0	0.0	0.0
40	2	0.3	0.3
41	0	0.0	0.0
44	1	0.2	0.2
45	0	0.0	0.0
46	0	0.0	0.0
48	0	0.0	0.0
50	2	0.3	0.3
51	1	0.2	0.2
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	0.2	0.2
56	0	0.0	0.0
57	1	0.2	0.2
58	2	0.3	0.3
59	0	0.0	0.0
60	1	0.2	0.2
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	1	0.2	0.2
69	1	0.2	0.2
70	0	0.0	0.0
71	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
79	1	0.2	0.2
85	0	0.0	0.0
“NA”	569	97.3	97.3
Total	585	100.0	100.0

Diabetes
	n	%	val%
No	366	62.6	64.6
Yes	200	34.2	35.3
Scantron_Error	1	0.2	0.2
NA	18	3.1	NA
Total	585	100.0	100.0

Diabetes age
	n	%	val%
0	3	0.5	0.5
10	0	0.0	0.0
12	1	0.2	0.2
13	0	0.0	0.0
14	1	0.2	0.2
15	0	0.0	0.0
16	0	0.0	0.0
17	1	0.2	0.2
18	1	0.2	0.2
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	1	0.2	0.2
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	1	0.2	0.2
36	2	0.3	0.3
37	1	0.2	0.2
38	2	0.3	0.3
39	1	0.2	0.2
40	7	1.2	1.2
41	0	0.0	0.0
42	1	0.2	0.2
43	3	0.5	0.5
44	0	0.0	0.0
45	7	1.2	1.2
46	1	0.2	0.2
47	0	0.0	0.0
48	5	0.9	0.9
49	3	0.5	0.5
5	0	0.0	0.0
50	12	2.1	2.1
51	3	0.5	0.5
52	3	0.5	0.5
53	3	0.5	0.5
54	4	0.7	0.7
55	16	2.7	2.7
56	5	0.9	0.9
57	4	0.7	0.7
58	11	1.9	1.9
59	7	1.2	1.2
60	15	2.6	2.6
61	7	1.2	1.2
62	7	1.2	1.2
63	1	0.2	0.2
64	5	0.9	0.9
65	5	0.9	0.9
66	2	0.3	0.3
67	0	0.0	0.0
68	3	0.5	0.5
69	4	0.7	0.7
70	7	1.2	1.2
71	1	0.2	0.2
72	0	0.0	0.0
73	0	0.0	0.0
74	1	0.2	0.2
75	1	0.2	0.2
76	0	0.0	0.0
78	0	0.0	0.0
81	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
97	0	0.0	0.0
“NA”	416	71.1	71.1
Total	585	100.0	100.0

Kidney Problems
	n	%	val%
No	532	90.9	94.7
Yes	30	5.1	5.3
Scantron_Error	0	0.0	0.0
NA	23	3.9	NA
Total	585	100.0	100.0

Kidney Problems age
	n	%	val%
11	1	0.2	0.2
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
29	0	0.0	0.0
35	0	0.0	0.0
38	1	0.2	0.2
40	0	0.0	0.0
42	0	0.0	0.0
44	1	0.2	0.2
45	1	0.2	0.2
46	0	0.0	0.0
47	1	0.2	0.2
48	0	0.0	0.0
49	0	0.0	0.0
5	2	0.3	0.3
50	1	0.2	0.2
51	1	0.2	0.2
52	0	0.0	0.0
53	1	0.2	0.2
54	0	0.0	0.0
55	2	0.3	0.3
56	0	0.0	0.0
57	0	0.0	0.0
58	1	0.2	0.2
59	0	0.0	0.0
60	1	0.2	0.2
61	1	0.2	0.2
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	2	0.3	0.3
66	0	0.0	0.0
67	1	0.2	0.2
68	1	0.2	0.2
69	2	0.3	0.3
7	0	0.0	0.0
70	1	0.2	0.2
71	0	0.0	0.0
72	0	0.0	0.0
74	0	0.0	0.0
75	1	0.2	0.2
76	0	0.0	0.0
77	1	0.2	0.2
80	0	0.0	0.0
83	0	0.0	0.0
95	1	0.2	0.2
“NA”	560	95.7	95.7
Total	585	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	550	94.0	97.7
Yes	13	2.2	2.3
Scantron_Error	0	0.0	0.0
NA	22	3.8	NA
Total	585	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	0	0.0	0.0
21	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
45	0	0.0	0.0
47	1	0.2	0.2
48	1	0.2	0.2
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
53	1	0.2	0.2
54	0	0.0	0.0
55	1	0.2	0.2
58	0	0.0	0.0
60	1	0.2	0.2
61	1	0.2	0.2
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.2	0.2
66	1	0.2	0.2
67	1	0.2	0.2
68	0	0.0	0.0
69	0	0.0	0.0
74	0	0.0	0.0
77	1	0.2	0.2
“NA”	575	98.3	98.3
Total	585	100.0	100.0

Arthritis
	n	%	val%
No	472	80.7	84.3
Yes	88	15.0	15.7
Scantron_Error	0	0.0	0.0
NA	25	4.3	NA
Total	585	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	1	0.2	0.2
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	1	0.2	0.2
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
28	0	0.0	0.0
30	1	0.2	0.2
32	0	0.0	0.0
33	0	0.0	0.0
35	3	0.5	0.5
36	0	0.0	0.0
38	2	0.3	0.3
39	2	0.3	0.3
4	0	0.0	0.0
40	2	0.3	0.3
41	1	0.2	0.2
42	0	0.0	0.0
43	1	0.2	0.2
44	1	0.2	0.2
45	6	1.0	1.0
46	0	0.0	0.0
47	0	0.0	0.0
48	3	0.5	0.5
49	0	0.0	0.0
50	7	1.2	1.2
51	1	0.2	0.2
52	6	1.0	1.0
53	2	0.3	0.3
54	1	0.2	0.2
55	4	0.7	0.7
56	0	0.0	0.0
57	0	0.0	0.0
58	4	0.7	0.7
59	1	0.2	0.2
60	5	0.9	0.9
61	0	0.0	0.0
62	2	0.3	0.3
63	1	0.2	0.2
64	3	0.5	0.5
65	1	0.2	0.2
66	0	0.0	0.0
67	1	0.2	0.2
68	2	0.3	0.3
69	2	0.3	0.3
7	0	0.0	0.0
70	0	0.0	0.0
71	1	0.2	0.2
72	2	0.3	0.3
73	0	0.0	0.0
74	1	0.2	0.2
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
“NA”	514	87.9	87.9
Total	585	100.0	100.0

Dementia
	n	%	val%
No	559	95.6	98.4
Yes	9	1.5	1.6
Scantron_Error	0	0.0	0.0
NA	17	2.9	NA
Total	585	100.0	100.0

Dementia age
	n	%	val%
10	0	0.0	0.0
29	0	0.0	0.0
53	0	0.0	0.0
57	0	0.0	0.0
60	0	0.0	0.0
62	1	0.2	0.2
63	0	0.0	0.0
64	0	0.0	0.0
65	1	0.2	0.2
66	0	0.0	0.0
67	0	0.0	0.0
69	1	0.2	0.2
70	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
80	1	0.2	0.2
“NA”	581	99.3	99.3
Total	585	100.0	100.0

Depression
	n	%	val%
No	486	83.1	87.1
Yes	72	12.3	12.9
Scantron_Error	0	0.0	0.0
NA	27	4.6	NA
Total	585	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
20	2	0.3	0.3
21	2	0.3	0.3
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	1	0.2	0.2
28	1	0.2	0.2
29	0	0.0	0.0
30	2	0.3	0.3
32	1	0.2	0.2
33	0	0.0	0.0
34	0	0.0	0.0
35	2	0.3	0.3
36	0	0.0	0.0
37	0	0.0	0.0
38	1	0.2	0.2
39	0	0.0	0.0
4	0	0.0	0.0
40	4	0.7	0.7
41	1	0.2	0.2
42	3	0.5	0.5
43	0	0.0	0.0
44	0	0.0	0.0
45	1	0.2	0.2
46	1	0.2	0.2
47	2	0.3	0.3
48	0	0.0	0.0
49	0	0.0	0.0
50	6	1.0	1.0
51	1	0.2	0.2
52	2	0.3	0.3
53	1	0.2	0.2
54	1	0.2	0.2
55	3	0.5	0.5
56	1	0.2	0.2
57	0	0.0	0.0
58	1	0.2	0.2
59	1	0.2	0.2
6	0	0.0	0.0
60	3	0.5	0.5
61	2	0.3	0.3
62	3	0.5	0.5
63	0	0.0	0.0
64	0	0.0	0.0
65	3	0.5	0.5
66	1	0.2	0.2
67	0	0.0	0.0
68	2	0.3	0.3
69	0	0.0	0.0
7	1	0.2	0.2
70	3	0.5	0.5
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
“NA”	526	89.9	89.9
Total	585	100.0	100.0

AIDS
	n	%	val%
No	562	96.1	99.5
Yes	3	0.5	0.5
Scantron_Error	0	0.0	0.0
NA	20	3.4	NA
Total	585	100.0	100.0

AIDS age
	n	%	val%
0	2	0.3	0.3
25	0	0.0	0.0
30	0	0.0	0.0
33	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
45	1	0.2	0.2
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
55	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
63	0	0.0	0.0
65	0	0.0	0.0
9	0	0.0	0.0
90	0	0.0	0.0
“NA”	582	99.5	99.5
Total	585	100.0	100.0

Other Cancer
	n	%	val%
No	522	89.2	93.2
Yes	38	6.5	6.8
Scantron_Error	0	0.0	0.0
NA	25	4.3	NA
Total	585	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	1	0.2	0.2
16	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	1	0.2	0.2
20	1	0.2	0.2
22	0	0.0	0.0
24	0	0.0	0.0
30	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	1	0.2	0.2
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	2	0.3	0.3
43	1	0.2	0.2
44	1	0.2	0.2
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	1	0.2	0.2
50	1	0.2	0.2
51	0	0.0	0.0
52	1	0.2	0.2
54	0	0.0	0.0
55	1	0.2	0.2
56	2	0.3	0.3
57	1	0.2	0.2
58	2	0.3	0.3
59	0	0.0	0.0
60	1	0.2	0.2
61	2	0.3	0.3
62	2	0.3	0.3
63	1	0.2	0.2
64	1	0.2	0.2
65	1	0.2	0.2
66	1	0.2	0.2
67	0	0.0	0.0
68	2	0.3	0.3
69	2	0.3	0.3
7	0	0.0	0.0
70	1	0.2	0.2
71	1	0.2	0.2
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	1	0.2	0.2
“NA”	552	94.4	94.4
Total	585	100.0	100.0

Georgia

Heart Attack
	n	%	val%
No	1532	87.3	93.2
Yes	110	6.3	6.7
Scantron_Error	2	0.1	0.1
NA	110	6.3	NA
Total	1754	100.0	100.0

Heart Attack age
	n	%	val%
0	21	1.2	1.2
1	1	0.1	0.1
14	0	0.0	0.0
17	1	0.1	0.1
20	0	0.0	0.0
24	1	0.1	0.1
25	1	0.1	0.1
26	0	0.0	0.0
27	2	0.1	0.1
29	1	0.1	0.1
31	2	0.1	0.1
32	0	0.0	0.0
34	1	0.1	0.1
35	2	0.1	0.1
38	1	0.1	0.1
40	2	0.1	0.1
42	0	0.0	0.0
44	0	0.0	0.0
45	1	0.1	0.1
46	3	0.2	0.2
47	1	0.1	0.1
48	3	0.2	0.2
49	1	0.1	0.1
5	1	0.1	0.1
50	8	0.5	0.5
51	0	0.0	0.0
52	3	0.2	0.2
53	1	0.1	0.1
54	1	0.1	0.1
55	4	0.2	0.2
56	1	0.1	0.1
57	4	0.2	0.2
58	6	0.3	0.3
59	6	0.3	0.3
60	5	0.3	0.3
61	3	0.2	0.2
62	1	0.1	0.1
63	5	0.3	0.3
64	4	0.2	0.2
65	2	0.1	0.1
66	1	0.1	0.1
67	3	0.2	0.2
68	2	0.1	0.1
69	3	0.2	0.2
70	1	0.1	0.1
71	2	0.1	0.1
72	0	0.0	0.0
73	2	0.1	0.1
74	3	0.2	0.2
76	0	0.0	0.0
79	0	0.0	0.0
93	0	0.0	0.0
“NA”	1637	93.3	93.3
Total	1754	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	1516	86.4	93.5
Yes	105	6.0	6.5
Scantron_Error	0	0.0	0.0
NA	133	7.6	NA
Total	1754	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
20	1	0.1	0.1
25	0	0.0	0.0
27	1	0.1	0.1
28	0	0.0	0.0
29	0	0.0	0.0
30	1	0.1	0.1
31	1	0.1	0.1
34	1	0.1	0.1
35	1	0.1	0.1
39	1	0.1	0.1
40	2	0.1	0.1
42	0	0.0	0.0
43	2	0.1	0.1
45	0	0.0	0.0
46	0	0.0	0.0
47	1	0.1	0.1
48	3	0.2	0.2
49	5	0.3	0.3
5	1	0.1	0.1
50	2	0.1	0.1
51	1	0.1	0.1
52	3	0.2	0.2
53	2	0.1	0.1
54	2	0.1	0.1
55	4	0.2	0.2
56	1	0.1	0.1
57	2	0.1	0.1
58	5	0.3	0.3
59	4	0.2	0.2
60	4	0.2	0.2
61	3	0.2	0.2
62	4	0.2	0.2
63	2	0.1	0.1
64	0	0.0	0.0
65	6	0.3	0.3
66	2	0.1	0.1
67	3	0.2	0.2
68	2	0.1	0.1
69	5	0.3	0.3
7	0	0.0	0.0
70	1	0.1	0.1
71	1	0.1	0.1
72	2	0.1	0.1
73	1	0.1	0.1
74	2	0.1	0.1
75	1	0.1	0.1
77	0	0.0	0.0
78	0	0.0	0.0
80	0	0.0	0.0
82	1	0.1	0.1
97	0	0.0	0.0
“NA”	1667	95.0	95.0
Total	1754	100.0	100.0

Stroke
	n	%	val%
No	1473	84.0	90.6
Yes	150	8.6	9.2
Scantron_Error	3	0.2	0.2
NA	128	7.3	NA
Total	1754	100.0	100.0

Stroke age
	n	%	val%
0	36	2.1	2.1
15	1	0.1	0.1
16	1	0.1	0.1
19	1	0.1	0.1
20	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
30	1	0.1	0.1
34	1	0.1	0.1
35	1	0.1	0.1
38	1	0.1	0.1
40	1	0.1	0.1
42	1	0.1	0.1
43	2	0.1	0.1
45	3	0.2	0.2
46	2	0.1	0.1
47	0	0.0	0.0
48	1	0.1	0.1
49	1	0.1	0.1
5	1	0.1	0.1
50	7	0.4	0.4
51	2	0.1	0.1
52	1	0.1	0.1
53	2	0.1	0.1
54	4	0.2	0.2
55	7	0.4	0.4
56	4	0.2	0.2
57	4	0.2	0.2
58	3	0.2	0.2
59	5	0.3	0.3
60	5	0.3	0.3
61	6	0.3	0.3
62	5	0.3	0.3
63	9	0.5	0.5
64	4	0.2	0.2
65	7	0.4	0.4
66	4	0.2	0.2
67	5	0.3	0.3
68	3	0.2	0.2
69	7	0.4	0.4
70	2	0.1	0.1
71	2	0.1	0.1
72	0	0.0	0.0
73	1	0.1	0.1
74	2	0.1	0.1
75	1	0.1	0.1
78	2	0.1	0.1
79	1	0.1	0.1
8	1	0.1	0.1
“NA”	1593	90.8	90.8
Total	1754	100.0	100.0

Hypertension
	n	%	val%
No	398	22.7	23.8
Yes	1270	72.4	76.0
Scantron_Error	2	0.1	0.1
NA	84	4.8	NA
Total	1754	100.0	100.0

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	1	0.1	0.1
10	0	0.0	0.0
12	1	0.1	0.1
13	1	0.1	0.1
14	1	0.1	0.1
15	3	0.2	0.2
16	6	0.3	0.3
17	1	0.1	0.1
18	4	0.2	0.2
19	3	0.2	0.2
2	1	0.1	0.1
20	6	0.3	0.3
21	2	0.1	0.1
22	3	0.2	0.2
23	3	0.2	0.2
24	4	0.2	0.2
25	10	0.6	0.6
26	1	0.1	0.1
27	5	0.3	0.3
28	6	0.3	0.3
29	1	0.1	0.1
30	37	2.1	2.1
31	9	0.5	0.5
32	4	0.2	0.2
33	4	0.2	0.2
34	4	0.2	0.2
35	47	2.7	2.7
36	10	0.6	0.6
37	4	0.2	0.2
38	10	0.6	0.6
39	7	0.4	0.4
4	1	0.1	0.1
40	90	5.1	5.1
41	5	0.3	0.3
42	18	1.0	1.0
43	7	0.4	0.4
44	9	0.5	0.5
45	76	4.3	4.3
46	12	0.7	0.7
47	10	0.6	0.6
48	17	1.0	1.0
49	14	0.8	0.8
5	1	0.1	0.1
50	115	6.6	6.6
51	11	0.6	0.6
52	28	1.6	1.6
53	11	0.6	0.6
54	24	1.4	1.4
55	80	4.6	4.6
56	21	1.2	1.2
57	18	1.0	1.0
58	28	1.6	1.6
59	18	1.0	1.0
6	0	0.0	0.0
60	76	4.3	4.3
61	15	0.9	0.9
62	24	1.4	1.4
63	12	0.7	0.7
64	19	1.1	1.1
65	30	1.7	1.7
66	6	0.3	0.3
67	14	0.8	0.8
68	12	0.7	0.7
69	9	0.5	0.5
7	0	0.0	0.0
70	15	0.9	0.9
71	9	0.5	0.5
72	3	0.2	0.2
73	2	0.1	0.1
74	0	0.0	0.0
75	1	0.1	0.1
77	1	0.1	0.1
78	0	0.0	0.0
79	0	0.0	0.0
8	2	0.1	0.1
80	0	0.0	0.0
89	1	0.1	0.1
9	0	0.0	0.0
92	1	0.1	0.1
94	1	0.1	0.1
96	1	0.1	0.1
98	0	0.0	0.0
99	1	0.1	0.1
“NA”	686	39.1	39.1
Total	1754	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	1488	84.8	93.4
Yes	104	5.9	6.5
Scantron_Error	1	0.1	0.1
NA	161	9.2	NA
Total	1754	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	1	0.1	0.1
**	1	0.1	0.1
0	16	0.9	0.9
1	1	0.1	0.1
16	1	0.1	0.1
17	0	0.0	0.0
19	1	0.1	0.1
25	0	0.0	0.0
26	1	0.1	0.1
30	1	0.1	0.1
31	0	0.0	0.0
33	1	0.1	0.1
34	2	0.1	0.1
35	4	0.2	0.2
36	0	0.0	0.0
37	1	0.1	0.1
40	7	0.4	0.4
41	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	3	0.2	0.2
46	0	0.0	0.0
47	1	0.1	0.1
48	1	0.1	0.1
5	0	0.0	0.0
50	8	0.5	0.5
51	0	0.0	0.0
52	1	0.1	0.1
53	0	0.0	0.0
54	3	0.2	0.2
55	8	0.5	0.5
56	1	0.1	0.1
57	1	0.1	0.1
58	5	0.3	0.3
59	3	0.2	0.2
60	14	0.8	0.8
61	0	0.0	0.0
62	6	0.3	0.3
63	2	0.1	0.1
64	0	0.0	0.0
65	10	0.6	0.6
66	2	0.1	0.1
67	5	0.3	0.3
68	3	0.2	0.2
69	3	0.2	0.2
70	1	0.1	0.1
71	1	0.1	0.1
72	2	0.1	0.1
73	2	0.1	0.1
74	2	0.1	0.1
76	0	0.0	0.0
77	0	0.0	0.0
78	2	0.1	0.1
82	0	0.0	0.0
84	1	0.1	0.1
94	0	0.0	0.0
97	1	0.1	0.1
“NA”	1624	92.6	92.6
Total	1754	100.0	100.0

High Cholesterol
	n	%	val%
No	720	41.0	44.0
Yes	916	52.2	56.0
Scantron_Error	1	0.1	0.1
NA	117	6.7	NA
Total	1754	100.0	100.0

High Cholesterol age
	n	%	val%
1	2	0.1	0.1
10	1	0.1	0.1
12	1	0.1	0.1
14	1	0.1	0.1
15	1	0.1	0.1
16	2	0.1	0.1
17	0	0.0	0.0
18	0	0.0	0.0
19	1	0.1	0.1
2	1	0.1	0.1
20	1	0.1	0.1
21	2	0.1	0.1
22	1	0.1	0.1
24	3	0.2	0.2
25	3	0.2	0.2
26	0	0.0	0.0
27	3	0.2	0.2
28	4	0.2	0.2
29	0	0.0	0.0
30	10	0.6	0.6
31	4	0.2	0.2
32	3	0.2	0.2
33	0	0.0	0.0
34	3	0.2	0.2
35	18	1.0	1.0
36	8	0.5	0.5
37	2	0.1	0.1
38	9	0.5	0.5
39	2	0.1	0.1
4	1	0.1	0.1
40	45	2.6	2.6
41	3	0.2	0.2
42	3	0.2	0.2
43	4	0.2	0.2
44	4	0.2	0.2
45	42	2.4	2.4
46	8	0.5	0.5
47	7	0.4	0.4
48	12	0.7	0.7
49	7	0.4	0.4
5	1	0.1	0.1
50	90	5.1	5.1
51	7	0.4	0.4
52	12	0.7	0.7
53	9	0.5	0.5
54	18	1.0	1.0
55	65	3.7	3.7
56	16	0.9	0.9
57	22	1.3	1.3
58	14	0.8	0.8
59	16	0.9	0.9
6	0	0.0	0.0
60	66	3.8	3.8
61	11	0.6	0.6
62	31	1.8	1.8
63	10	0.6	0.6
64	11	0.6	0.6
65	24	1.4	1.4
66	7	0.4	0.4
67	11	0.6	0.6
68	8	0.5	0.5
69	16	0.9	0.9
7	0	0.0	0.0
70	14	0.8	0.8
71	2	0.1	0.1
72	8	0.5	0.5
73	2	0.1	0.1
74	3	0.2	0.2
75	2	0.1	0.1
76	3	0.2	0.2
77	0	0.0	0.0
8	1	0.1	0.1
80	0	0.0	0.0
86	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
95	1	0.1	0.1
96	1	0.1	0.1
97	1	0.1	0.1
99	0	0.0	0.0
“NA”	1029	58.7	58.7
Total	1754	100.0	100.0

Asthma, COPD
	n	%	val%
No	1464	83.5	86.3
Yes	232	13.2	13.7
Scantron_Error	0	0.0	0.0
NA	58	3.3	NA
Total	1754	100.0	100.0

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	4	0.2	0.2
10	9	0.5	0.5
11	1	0.1	0.1
12	6	0.3	0.3
13	1	0.1	0.1
14	1	0.1	0.1
15	3	0.2	0.2
16	3	0.2	0.2
17	0	0.0	0.0
18	8	0.5	0.5
19	0	0.0	0.0
2	3	0.2	0.2
20	0	0.0	0.0
21	1	0.1	0.1
23	1	0.1	0.1
25	3	0.2	0.2
26	0	0.0	0.0
27	1	0.1	0.1
28	2	0.1	0.1
29	1	0.1	0.1
3	0	0.0	0.0
30	4	0.2	0.2
31	0	0.0	0.0
32	1	0.1	0.1
33	1	0.1	0.1
35	1	0.1	0.1
37	1	0.1	0.1
38	3	0.2	0.2
39	0	0.0	0.0
4	3	0.2	0.2
40	6	0.3	0.3
42	3	0.2	0.2
43	1	0.1	0.1
45	4	0.2	0.2
46	1	0.1	0.1
47	1	0.1	0.1
48	1	0.1	0.1
49	2	0.1	0.1
5	12	0.7	0.7
50	7	0.4	0.4
51	1	0.1	0.1
52	3	0.2	0.2
53	1	0.1	0.1
54	1	0.1	0.1
55	9	0.5	0.5
56	3	0.2	0.2
57	5	0.3	0.3
58	3	0.2	0.2
59	6	0.3	0.3
6	7	0.4	0.4
60	9	0.5	0.5
61	2	0.1	0.1
62	5	0.3	0.3
63	3	0.2	0.2
64	2	0.1	0.1
65	9	0.5	0.5
66	1	0.1	0.1
67	3	0.2	0.2
68	5	0.3	0.3
69	3	0.2	0.2
7	2	0.1	0.1
70	2	0.1	0.1
71	3	0.2	0.2
72	2	0.1	0.1
73	3	0.2	0.2
74	0	0.0	0.0
75	0	0.0	0.0
77	1	0.1	0.1
78	0	0.0	0.0
79	0	0.0	0.0
8	2	0.1	0.1
80	0	0.0	0.0
81	0	0.0	0.0
9	5	0.3	0.3
93	0	0.0	0.0
97	0	0.0	0.0
“NA”	1553	88.5	88.5
Total	1754	100.0	100.0

Stomach ulcers
	n	%	val%
No	1536	87.6	90.8
Yes	156	8.9	9.2
Scantron_Error	0	0.0	0.0
NA	62	3.5	NA
Total	1754	100.0	100.0

Stomach ulcers age
	n	%	val%
0	20	1.1	1.1
10	1	0.1	0.1
13	0	0.0	0.0
14	2	0.1	0.1
15	1	0.1	0.1
16	4	0.2	0.2
17	0	0.0	0.0
18	2	0.1	0.1
19	0	0.0	0.0
2	1	0.1	0.1
20	3	0.2	0.2
21	0	0.0	0.0
22	2	0.1	0.1
23	2	0.1	0.1
24	2	0.1	0.1
25	4	0.2	0.2
27	2	0.1	0.1
28	2	0.1	0.1
3	0	0.0	0.0
30	6	0.3	0.3
32	2	0.1	0.1
34	1	0.1	0.1
35	8	0.5	0.5
36	2	0.1	0.1
38	0	0.0	0.0
39	2	0.1	0.1
40	12	0.7	0.7
42	0	0.0	0.0
44	1	0.1	0.1
45	8	0.5	0.5
46	0	0.0	0.0
47	3	0.2	0.2
48	4	0.2	0.2
49	1	0.1	0.1
50	6	0.3	0.3
52	1	0.1	0.1
53	1	0.1	0.1
54	0	0.0	0.0
55	3	0.2	0.2
56	2	0.1	0.1
57	2	0.1	0.1
58	2	0.1	0.1
59	1	0.1	0.1
60	2	0.1	0.1
61	1	0.1	0.1
62	7	0.4	0.4
63	3	0.2	0.2
64	2	0.1	0.1
65	2	0.1	0.1
66	1	0.1	0.1
67	4	0.2	0.2
68	1	0.1	0.1
69	2	0.1	0.1
7	0	0.0	0.0
70	1	0.1	0.1
71	1	0.1	0.1
72	2	0.1	0.1
73	0	0.0	0.0
74	1	0.1	0.1
76	1	0.1	0.1
8	1	0.1	0.1
80	0	0.0	0.0
9	1	0.1	0.1
94	1	0.1	0.1
“NA”	1604	91.4	91.4
Total	1754	100.0	100.0

Crohn’s Disease
	n	%	val%
No	1629	92.9	96.4
Yes	60	3.4	3.6
Scantron_Error	0	0.0	0.0
NA	65	3.7	NA
Total	1754	100.0	100.0

Crohn’s Disease age
	n	%	val%
15	0	0.0	0.0
2	1	0.1	0.1
22	0	0.0	0.0
30	1	0.1	0.1
32	1	0.1	0.1
33	1	0.1	0.1
34	0	0.0	0.0
35	1	0.1	0.1
37	1	0.1	0.1
39	1	0.1	0.1
40	6	0.3	0.3
41	0	0.0	0.0
44	2	0.1	0.1
45	1	0.1	0.1
46	1	0.1	0.1
48	1	0.1	0.1
50	2	0.1	0.1
51	1	0.1	0.1
52	2	0.1	0.1
53	1	0.1	0.1
54	2	0.1	0.1
55	3	0.2	0.2
56	2	0.1	0.1
57	0	0.0	0.0
58	2	0.1	0.1
59	1	0.1	0.1
60	4	0.2	0.2
62	1	0.1	0.1
63	2	0.1	0.1
64	0	0.0	0.0
65	1	0.1	0.1
66	1	0.1	0.1
67	0	0.0	0.0
69	2	0.1	0.1
70	1	0.1	0.1
71	1	0.1	0.1
74	1	0.1	0.1
75	1	0.1	0.1
76	1	0.1	0.1
79	0	0.0	0.0
85	1	0.1	0.1
“NA”	1703	97.1	97.1
Total	1754	100.0	100.0

Diabetes
	n	%	val%
No	1173	66.9	68.6
Yes	534	30.4	31.2
Scantron_Error	3	0.2	0.2
NA	44	2.5	NA
Total	1754	100.0	100.0

Diabetes age
	n	%	val%
0	5	0.3	0.3
10	1	0.1	0.1
12	1	0.1	0.1
13	2	0.1	0.1
14	1	0.1	0.1
15	2	0.1	0.1
16	1	0.1	0.1
17	1	0.1	0.1
18	0	0.0	0.0
19	0	0.0	0.0
2	1	0.1	0.1
20	1	0.1	0.1
22	0	0.0	0.0
24	1	0.1	0.1
27	1	0.1	0.1
28	1	0.1	0.1
29	1	0.1	0.1
3	0	0.0	0.0
30	7	0.4	0.4
31	1	0.1	0.1
32	1	0.1	0.1
34	1	0.1	0.1
35	13	0.7	0.7
36	0	0.0	0.0
37	3	0.2	0.2
38	2	0.1	0.1
39	3	0.2	0.2
40	23	1.3	1.3
41	2	0.1	0.1
42	5	0.3	0.3
43	7	0.4	0.4
44	6	0.3	0.3
45	26	1.5	1.5
46	6	0.3	0.3
47	7	0.4	0.4
48	6	0.3	0.3
49	3	0.2	0.2
5	1	0.1	0.1
50	43	2.5	2.5
51	5	0.3	0.3
52	8	0.5	0.5
53	11	0.6	0.6
54	16	0.9	0.9
55	36	2.1	2.1
56	13	0.7	0.7
57	8	0.5	0.5
58	15	0.9	0.9
59	15	0.9	0.9
60	34	1.9	1.9
61	13	0.7	0.7
62	17	1.0	1.0
63	9	0.5	0.5
64	10	0.6	0.6
65	18	1.0	1.0
66	5	0.3	0.3
67	4	0.2	0.2
68	6	0.3	0.3
69	7	0.4	0.4
70	12	0.7	0.7
71	4	0.2	0.2
72	3	0.2	0.2
73	1	0.1	0.1
74	2	0.1	0.1
75	2	0.1	0.1
76	0	0.0	0.0
78	1	0.1	0.1
81	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
97	1	0.1	0.1
“NA”	1292	73.7	73.7
Total	1754	100.0	100.0

Kidney Problems
	n	%	val%
No	1597	91.0	93.7
Yes	107	6.1	6.3
Scantron_Error	1	0.1	0.1
NA	49	2.8	NA
Total	1754	100.0	100.0

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	1	0.1	0.1
16	1	0.1	0.1
17	1	0.1	0.1
29	0	0.0	0.0
35	1	0.1	0.1
38	0	0.0	0.0
40	3	0.2	0.2
42	1	0.1	0.1
44	0	0.0	0.0
45	1	0.1	0.1
46	1	0.1	0.1
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	4	0.2	0.2
51	2	0.1	0.1
52	1	0.1	0.1
53	2	0.1	0.1
54	5	0.3	0.3
55	5	0.3	0.3
56	6	0.3	0.3
57	1	0.1	0.1
58	1	0.1	0.1
59	2	0.1	0.1
60	1	0.1	0.1
61	3	0.2	0.2
62	6	0.3	0.3
63	1	0.1	0.1
64	4	0.2	0.2
65	7	0.4	0.4
66	2	0.1	0.1
67	4	0.2	0.2
68	1	0.1	0.1
69	3	0.2	0.2
7	1	0.1	0.1
70	4	0.2	0.2
71	1	0.1	0.1
72	1	0.1	0.1
74	1	0.1	0.1
75	3	0.2	0.2
76	1	0.1	0.1
77	0	0.0	0.0
80	0	0.0	0.0
83	0	0.0	0.0
95	0	0.0	0.0
“NA”	1671	95.3	95.3
Total	1754	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	1666	95.0	98.6
Yes	24	1.4	1.4
Scantron_Error	0	0.0	0.0
NA	64	3.6	NA
Total	1754	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	0	0.0	0.0
21	0	0.0	0.0
39	1	0.1	0.1
40	1	0.1	0.1
42	1	0.1	0.1
45	2	0.1	0.1
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	1	0.1	0.1
53	0	0.0	0.0
54	1	0.1	0.1
55	2	0.1	0.1
58	0	0.0	0.0
60	3	0.2	0.2
61	0	0.0	0.0
63	1	0.1	0.1
64	1	0.1	0.1
65	2	0.1	0.1
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	1	0.1	0.1
74	0	0.0	0.0
77	0	0.0	0.0
“NA”	1737	99.0	99.0
Total	1754	100.0	100.0

Arthritis
	n	%	val%
No	1495	85.2	88.1
Yes	202	11.5	11.9
Scantron_Error	0	0.0	0.0
NA	57	3.2	NA
Total	1754	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
2	1	0.1	0.1
20	2	0.1	0.1
21	0	0.0	0.0
22	1	0.1	0.1
23	0	0.0	0.0
24	1	0.1	0.1
25	3	0.2	0.2
28	0	0.0	0.0
30	4	0.2	0.2
32	0	0.0	0.0
33	1	0.1	0.1
35	2	0.1	0.1
36	1	0.1	0.1
38	0	0.0	0.0
39	1	0.1	0.1
4	1	0.1	0.1
40	9	0.5	0.5
41	2	0.1	0.1
42	2	0.1	0.1
43	3	0.2	0.2
44	0	0.0	0.0
45	5	0.3	0.3
46	1	0.1	0.1
47	4	0.2	0.2
48	1	0.1	0.1
49	1	0.1	0.1
50	17	1.0	1.0
51	3	0.2	0.2
52	0	0.0	0.0
53	2	0.1	0.1
54	1	0.1	0.1
55	18	1.0	1.0
56	3	0.2	0.2
57	2	0.1	0.1
58	7	0.4	0.4
59	0	0.0	0.0
60	18	1.0	1.0
61	3	0.2	0.2
62	4	0.2	0.2
63	2	0.1	0.1
64	2	0.1	0.1
65	12	0.7	0.7
66	2	0.1	0.1
67	5	0.3	0.3
68	4	0.2	0.2
69	2	0.1	0.1
7	1	0.1	0.1
70	5	0.3	0.3
71	2	0.1	0.1
72	1	0.1	0.1
73	1	0.1	0.1
74	0	0.0	0.0
75	2	0.1	0.1
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
“NA”	1589	90.6	90.6
Total	1754	100.0	100.0

Dementia
	n	%	val%
No	1677	95.6	98.5
Yes	25	1.4	1.5
Scantron_Error	1	0.1	0.1
NA	51	2.9	NA
Total	1754	100.0	100.0

Dementia age
	n	%	val%
10	1	0.1	0.1
29	0	0.0	0.0
53	0	0.0	0.0
57	0	0.0	0.0
60	0	0.0	0.0
62	1	0.1	0.1
63	0	0.0	0.0
64	1	0.1	0.1
65	1	0.1	0.1
66	1	0.1	0.1
67	1	0.1	0.1
69	0	0.0	0.0
70	5	0.3	0.3
72	1	0.1	0.1
73	2	0.1	0.1
74	1	0.1	0.1
75	1	0.1	0.1
80	0	0.0	0.0
“NA”	1738	99.1	99.1
Total	1754	100.0	100.0

Depression
	n	%	val%
No	1471	83.9	86.8
Yes	223	12.7	13.2
Scantron_Error	1	0.1	0.1
NA	59	3.4	NA
Total	1754	100.0	100.0

Depression age
	n	%	val%
1	1	0.1	0.1
14	0	0.0	0.0
16	0	0.0	0.0
17	1	0.1	0.1
18	1	0.1	0.1
19	2	0.1	0.1
20	1	0.1	0.1
21	3	0.2	0.2
22	0	0.0	0.0
24	0	0.0	0.0
25	1	0.1	0.1
26	2	0.1	0.1
28	3	0.2	0.2
29	1	0.1	0.1
30	1	0.1	0.1
32	3	0.2	0.2
33	0	0.0	0.0
34	0	0.0	0.0
35	4	0.2	0.2
36	3	0.2	0.2
37	3	0.2	0.2
38	1	0.1	0.1
39	0	0.0	0.0
4	1	0.1	0.1
40	8	0.5	0.5
41	1	0.1	0.1
42	1	0.1	0.1
43	3	0.2	0.2
44	1	0.1	0.1
45	7	0.4	0.4
46	2	0.1	0.1
47	4	0.2	0.2
48	4	0.2	0.2
49	5	0.3	0.3
50	14	0.8	0.8
51	1	0.1	0.1
52	4	0.2	0.2
53	3	0.2	0.2
54	2	0.1	0.1
55	12	0.7	0.7
56	5	0.3	0.3
57	3	0.2	0.2
58	6	0.3	0.3
59	4	0.2	0.2
6	0	0.0	0.0
60	8	0.5	0.5
61	3	0.2	0.2
62	3	0.2	0.2
63	3	0.2	0.2
64	3	0.2	0.2
65	8	0.5	0.5
66	3	0.2	0.2
67	3	0.2	0.2
68	5	0.3	0.3
69	1	0.1	0.1
7	1	0.1	0.1
70	2	0.1	0.1
72	1	0.1	0.1
73	1	0.1	0.1
74	0	0.0	0.0
75	0	0.0	0.0
76	1	0.1	0.1
8	1	0.1	0.1
91	1	0.1	0.1
96	1	0.1	0.1
98	1	0.1	0.1
“NA”	1582	90.2	90.2
Total	1754	100.0	100.0

AIDS
	n	%	val%
No	1675	95.5	98.8
Yes	20	1.1	1.2
Scantron_Error	0	0.0	0.0
NA	59	3.4	NA
Total	1754	100.0	100.0

AIDS age
	n	%	val%
0	21	1.2	1.2
25	1	0.1	0.1
30	0	0.0	0.0
33	0	0.0	0.0
36	1	0.1	0.1
38	1	0.1	0.1
39	1	0.1	0.1
40	2	0.1	0.1
45	0	0.0	0.0
48	0	0.0	0.0
49	1	0.1	0.1
50	1	0.1	0.1
51	1	0.1	0.1
55	0	0.0	0.0
59	1	0.1	0.1
60	1	0.1	0.1
63	0	0.0	0.0
65	0	0.0	0.0
9	0	0.0	0.0
90	1	0.1	0.1
“NA”	1721	98.1	98.1
Total	1754	100.0	100.0

Other Cancer
	n	%	val%
No	1564	89.2	92.9
Yes	120	6.8	7.1
Scantron_Error	0	0.0	0.0
NA	70	4.0	NA
Total	1754	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	0	0.0	0.0
16	1	0.1	0.1
18	1	0.1	0.1
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	1	0.1	0.1
30	1	0.1	0.1
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	1	0.1	0.1
40	1	0.1	0.1
41	0	0.0	0.0
42	1	0.1	0.1
43	1	0.1	0.1
44	0	0.0	0.0
45	1	0.1	0.1
47	1	0.1	0.1
48	0	0.0	0.0
49	4	0.2	0.2
50	1	0.1	0.1
51	1	0.1	0.1
52	1	0.1	0.1
54	4	0.2	0.2
55	4	0.2	0.2
56	5	0.3	0.3
57	2	0.1	0.1
58	7	0.4	0.4
59	2	0.1	0.1
60	8	0.5	0.5
61	6	0.3	0.3
62	5	0.3	0.3
63	4	0.2	0.2
64	4	0.2	0.2
65	3	0.2	0.2
66	2	0.1	0.1
67	3	0.2	0.2
68	3	0.2	0.2
69	3	0.2	0.2
7	0	0.0	0.0
70	5	0.3	0.3
71	1	0.1	0.1
72	2	0.1	0.1
73	1	0.1	0.1
74	5	0.3	0.3
75	2	0.1	0.1
76	1	0.1	0.1
77	1	0.1	0.1
78	1	0.1	0.1
79	0	0.0	0.0
“NA”	1653	94.2	94.2
Total	1754	100.0	100.0

Michigan

Heart Attack
	n	%	val%
No	14	87.5	87.5
Yes	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Heart Attack age
	n	%	val%
0	0	0.0	0.0
1	0	0.0	0.0
14	0	0.0	0.0
17	0	0.0	0.0
20	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
29	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	6.2	6.2
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	1	6.2	6.2
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
79	0	0.0	0.0
93	0	0.0	0.0
“NA”	13	81.2	81.2
Total	16	100.0	100.0

Heart Failure or CHF
	n	%	val%
No	13	81.2	81.2
Yes	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Heart Failure or CHF age
	n	%	val%
12	0	0.0	0.0
13	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
20	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	1	6.2	6.2
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	1	6.2	6.2
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
80	0	0.0	0.0
82	0	0.0	0.0
97	0	0.0	0.0
“NA”	13	81.2	81.2
Total	16	100.0	100.0

Stroke
	n	%	val%
No	13	81.2	86.7
Yes	2	12.5	13.3
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

Stroke age
	n	%	val%
0	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
23	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
30	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	6.2	6.2
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	1	6.2	6.2
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
“NA”	14	87.5	87.5
Total	16	100.0	100.0

Hypertension
	n	%	val%
No	4	25	25
Yes	12	75	75
Scantron_Error	0	0	0
Total	16	100	100

Hypertension age
	n	%	val%
.35	0	0.0	0.0
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	1	6.2	6.2
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	1	6.2	6.2
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
41	2	12.5	12.5
42	0	0.0	0.0
43	0	0.0	0.0
44	1	6.2	6.2
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	1	6.2	6.2
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
6	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	1	6.2	6.2
64	0	0.0	0.0
65	1	6.2	6.2
66	1	6.2	6.2
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
89	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
99	0	0.0	0.0
“NA”	6	37.5	37.5
Total	16	100.0	100.0

Peripheral arterial disease
	n	%	val%
No	14	87.5	93.3
Yes	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

Peripheral arterial disease age
	n	%	val%
*	0	0.0	0.0
**	0	0.0	0.0
0	0	0.0	0.0
1	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
5	0	0.0	0.0
50	1	6.2	6.2
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
82	0	0.0	0.0
84	0	0.0	0.0
94	0	0.0	0.0
97	0	0.0	0.0
“NA”	15	93.8	93.8
Total	16	100.0	100.0

High Cholesterol
	n	%	val%
No	7	43.8	46.7
Yes	8	50.0	53.3
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

High Cholesterol age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	1	6.2	6.2
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
41	1	6.2	6.2
42	0	0.0	0.0
43	1	6.2	6.2
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	1	6.2	6.2
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	2	12.5	12.5
58	0	0.0	0.0
59	0	0.0	0.0
6	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	1	6.2	6.2
64	0	0.0	0.0
65	1	6.2	6.2
66	1	6.2	6.2
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
86	0	0.0	0.0
9	0	0.0	0.0
92	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
96	0	0.0	0.0
97	0	0.0	0.0
99	0	0.0	0.0
“NA”	7	43.8	43.8
Total	16	100.0	100.0

Asthma, COPD
	n	%	val%
No	11	68.8	73.3
Yes	4	25.0	26.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

Asthma, COPD age
	n	%	val%
06	0	0.0	0.0
1	0	0.0	0.0
10	0	0.0	0.0
11	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
23	0	0.0	0.0
25	1	6.2	6.2
26	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	1	6.2	6.2
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
6	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	1	6.2	6.2
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
81	0	0.0	0.0
9	0	0.0	0.0
93	0	0.0	0.0
97	0	0.0	0.0
“NA”	12	75.0	75.0
Total	16	100.0	100.0

Stomach ulcers
	n	%	val%
No	13	81.2	92.9
Yes	1	6.2	7.1
Scantron_Error	0	0.0	0.0
NA	2	12.5	NA
Total	16	100.0	100.0

Stomach ulcers age
	n	%	val%
0	0	0.0	0.0
10	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
27	0	0.0	0.0
28	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	6.2	6.2
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
80	0	0.0	0.0
9	0	0.0	0.0
94	0	0.0	0.0
“NA”	15	93.8	93.8
Total	16	100.0	100.0

Crohn’s Disease
	n	%	val%
No	14	87.5	100
Yes	0	0.0	0
Scantron_Error	0	0.0	0
NA	2	12.5	NA
Total	16	100.0	100

Crohn’s Disease age
	n	%	val%
15	0	0	0
2	0	0	0
22	0	0	0
30	0	0	0
32	0	0	0
33	0	0	0
34	0	0	0
35	0	0	0
37	0	0	0
39	0	0	0
40	0	0	0
41	0	0	0
44	0	0	0
45	0	0	0
46	0	0	0
48	0	0	0
50	0	0	0
51	0	0	0
52	0	0	0
53	0	0	0
54	0	0	0
55	0	0	0
56	0	0	0
57	0	0	0
58	0	0	0
59	0	0	0
60	0	0	0
62	0	0	0
63	0	0	0
64	0	0	0
65	0	0	0
66	0	0	0
67	0	0	0
69	0	0	0
70	0	0	0
71	0	0	0
74	0	0	0
75	0	0	0
76	0	0	0
79	0	0	0
85	0	0	0
“NA”	16	100	100
Total	16	100	100

Diabetes
	n	%	val%
No	7	43.8	43.8
Yes	9	56.2	56.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Diabetes age
	n	%	val%
0	0	0.0	0.0
10	0	0.0	0.0
12	0	0.0	0.0
13	0	0.0	0.0
14	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
27	0	0.0	0.0
28	1	6.2	6.2
29	0	0.0	0.0
3	0	0.0	0.0
30	0	0.0	0.0
31	0	0.0	0.0
32	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	1	6.2	6.2
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	1	6.2	6.2
48	0	0.0	0.0
49	1	6.2	6.2
5	0	0.0	0.0
50	1	6.2	6.2
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	1	6.2	6.2
64	0	0.0	0.0
65	0	0.0	0.0
66	1	6.2	6.2
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	1	6.2	6.2
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
78	0	0.0	0.0
81	0	0.0	0.0
94	0	0.0	0.0
95	0	0.0	0.0
97	0	0.0	0.0
“NA”	7	43.8	43.8
Total	16	100.0	100.0

Kidney Problems
	n	%	val%
No	13	81.2	81.2
Yes	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Kidney Problems age
	n	%	val%
11	0	0.0	0.0
15	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
29	0	0.0	0.0
35	0	0.0	0.0
38	0	0.0	0.0
40	0	0.0	0.0
42	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
5	0	0.0	0.0
50	1	6.2	6.2
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	1	6.2	6.2
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
83	0	0.0	0.0
95	0	0.0	0.0
“NA”	14	87.5	87.5
Total	16	100.0	100.0

Cirrhosis, liver damage
	n	%	val%
No	15	93.8	93.8
Yes	1	6.2	6.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Cirrhosis, liver damage age
	n	%	val%
18	0	0	0
21	0	0	0
39	0	0	0
40	0	0	0
42	0	0	0
45	0	0	0
47	0	0	0
48	0	0	0
49	0	0	0
50	0	0	0
51	0	0	0
53	0	0	0
54	0	0	0
55	0	0	0
58	0	0	0
60	0	0	0
61	0	0	0
63	0	0	0
64	0	0	0
65	0	0	0
66	0	0	0
67	0	0	0
68	0	0	0
69	0	0	0
74	0	0	0
77	0	0	0
“NA”	16	100	100
Total	16	100	100

Arthritis
	n	%	val%
No	14	87.5	87.5
Yes	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Arthritis age
	n	%	val%
1	0	0.0	0.0
10	0	0.0	0.0
17	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
23	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
28	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	1	6.2	6.2
58	0	0.0	0.0
59	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
80	0	0.0	0.0
“NA”	15	93.8	93.8
Total	16	100.0	100.0

Dementia
	n	%	val%
No	16	100	100
Yes	0	0	0
Scantron_Error	0	0	0
Total	16	100	100

Dementia age
	n	%	val%
10	0	0	0
29	0	0	0
53	0	0	0
57	0	0	0
60	0	0	0
62	0	0	0
63	0	0	0
64	0	0	0
65	0	0	0
66	0	0	0
67	0	0	0
69	0	0	0
70	0	0	0
72	0	0	0
73	0	0	0
74	0	0	0
75	0	0	0
80	0	0	0
“NA”	16	100	100
Total	16	100	100

Depression
	n	%	val%
No	14	87.5	87.5
Yes	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Depression age
	n	%	val%
1	0	0.0	0.0
14	0	0.0	0.0
16	0	0.0	0.0
17	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
20	0	0.0	0.0
21	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
25	0	0.0	0.0
26	0	0.0	0.0
28	0	0.0	0.0
29	0	0.0	0.0
30	0	0.0	0.0
32	0	0.0	0.0
33	0	0.0	0.0
34	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
37	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
4	0	0.0	0.0
40	1	6.2	6.2
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
46	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
53	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
6	0	0.0	0.0
60	0	0.0	0.0
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
8	0	0.0	0.0
91	0	0.0	0.0
96	0	0.0	0.0
98	0	0.0	0.0
“NA”	15	93.8	93.8
Total	16	100.0	100.0

AIDS
	n	%	val%
No	16	100	100
Yes	0	0	0
Scantron_Error	0	0	0
Total	16	100	100

AIDS age
	n	%	val%
0	0	0	0
25	0	0	0
30	0	0	0
33	0	0	0
36	0	0	0
38	0	0	0
39	0	0	0
40	0	0	0
45	0	0	0
48	0	0	0
49	0	0	0
50	0	0	0
51	0	0	0
55	0	0	0
59	0	0	0
60	0	0	0
63	0	0	0
65	0	0	0
9	0	0	0
90	0	0	0
“NA”	16	100	100
Total	16	100	100

Other Cancer
	n	%	val%
No	15	93.8	93.8
Yes	1	6.2	6.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

Other Cancer age
	n	%	val%
10	0	0.0	0.0
12	0	0.0	0.0
16	0	0.0	0.0
18	0	0.0	0.0
19	0	0.0	0.0
2	0	0.0	0.0
20	0	0.0	0.0
22	0	0.0	0.0
24	0	0.0	0.0
30	0	0.0	0.0
35	0	0.0	0.0
36	0	0.0	0.0
38	0	0.0	0.0
39	0	0.0	0.0
40	0	0.0	0.0
41	0	0.0	0.0
42	0	0.0	0.0
43	0	0.0	0.0
44	0	0.0	0.0
45	0	0.0	0.0
47	0	0.0	0.0
48	0	0.0	0.0
49	0	0.0	0.0
50	0	0.0	0.0
51	0	0.0	0.0
52	0	0.0	0.0
54	0	0.0	0.0
55	0	0.0	0.0
56	0	0.0	0.0
57	0	0.0	0.0
58	0	0.0	0.0
59	0	0.0	0.0
60	1	6.2	6.2
61	0	0.0	0.0
62	0	0.0	0.0
63	0	0.0	0.0
64	0	0.0	0.0
65	0	0.0	0.0
66	0	0.0	0.0
67	0	0.0	0.0
68	0	0.0	0.0
69	0	0.0	0.0
7	0	0.0	0.0
70	0	0.0	0.0
71	0	0.0	0.0
72	0	0.0	0.0
73	0	0.0	0.0
74	0	0.0	0.0
75	0	0.0	0.0
76	0	0.0	0.0
77	0	0.0	0.0
78	0	0.0	0.0
79	0	0.0	0.0
“NA”	15	93.8	93.8
Total	16	100.0	100.0

B5

B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
- 1=Community health center or free clinic
- 2=Hospital (not emergency)/ urgent care clinic
- 3=Private doctor’s office
- 4=Emergency room
- 5=Veteran’s Affairs/VA
- 6=Other type of location

All data

  b5 <- as.factor(d[,"b5"])
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6",
                     Scantron_Error="*")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other","Scantron_Error"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')

	n	%	val%
Community_center_free_clinic	246	6.9	7.2
Hospital_urgent_care_clinic	263	7.4	7.7
Private_Dr_office	2277	64.0	66.7
ER	24	0.7	0.7
VA	361	10.1	10.6
Other	63	1.8	1.8
Scantron_Error	178	5.0	5.2
NA	145	4.1	NA
Total	3557	100.0	100.0

LA County

	n	%	val%
Community_center_free_clinic	21	6.5	6.6
Hospital_urgent_care_clinic	20	6.2	6.3
Private_Dr_office	242	75.4	76.6
ER	2	0.6	0.6
VA	13	4.0	4.1
Other	6	1.9	1.9
Scantron_Error	12	3.7	3.8
NA	5	1.6	NA
Total	321	100.0	100.0

Northern CA

	n	%	val%
Community_center_free_clinic	16	7.6	8.0
Hospital_urgent_care_clinic	22	10.5	10.9
Private_Dr_office	145	69.0	72.1
ER	1	0.5	0.5
VA	7	3.3	3.5
Other	3	1.4	1.5
Scantron_Error	7	3.3	3.5
NA	9	4.3	NA
Total	210	100.0	100.0

Greater CA

	n	%	val%
Community_center_free_clinic	23	7.3	7.7
Hospital_urgent_care_clinic	27	8.6	9.0
Private_Dr_office	216	68.6	72.2
ER	2	0.6	0.7
VA	18	5.7	6.0
Other	7	2.2	2.3
Scantron_Error	6	1.9	2.0
NA	16	5.1	NA
Total	315	100.0	100.0

Detroit

	n	%	val%
Community_center_free_clinic	25	7.0	7.4
Hospital_urgent_care_clinic	46	12.9	13.6
Private_Dr_office	231	64.9	68.1
ER	2	0.6	0.6
VA	10	2.8	2.9
Other	9	2.5	2.7
Scantron_Error	16	4.5	4.7
NA	17	4.8	NA
Total	356	100.0	100.0

Louisiana

	n	%	val%
Community_center_free_clinic	59	10.1	10.5
Hospital_urgent_care_clinic	58	9.9	10.4
Private_Dr_office	315	53.8	56.2
ER	4	0.7	0.7
VA	76	13.0	13.6
Other	6	1.0	1.1
Scantron_Error	42	7.2	7.5
NA	25	4.3	NA
Total	585	100.0	100.0

Georgia

	n	%	val%
Community_center_free_clinic	101	5.8	6.0
Hospital_urgent_care_clinic	87	5.0	5.2
Private_Dr_office	1119	63.8	66.5
ER	13	0.7	0.8
VA	237	13.5	14.1
Other	32	1.8	1.9
Scantron_Error	94	5.4	5.6
NA	71	4.0	NA
Total	1754	100.0	100.0

Michigan

	n	%	val%
Community_center_free_clinic	1	6.2	7.1
Hospital_urgent_care_clinic	3	18.8	21.4
Private_Dr_office	9	56.2	64.3
ER	0	0.0	0.0
VA	0	0.0	0.0
Other	0	0.0	0.0
Scantron_Error	1	6.2	7.1
NA	2	12.5	NA
Total	16	100.0	100.0

C1

C1. How many years have you lived in your current address?
- 1=Less than 1 year
- 2=1-5 years
- 3=6-10 years
- 4=11-15 years
- 5=16-20 years
- 6=21+ years

All data

  c1 <- as.factor(d[,"c1"])
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6",
                     Scantron_Error="*")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')

	n	%	val%	%cum	val%cum
Less_than_1_year	137	3.9	3.9	3.9	3.9
years_1_5	610	17.1	17.4	21.0	21.3
years_6_10	478	13.4	13.6	34.4	34.9
years_11_15	413	11.6	11.8	46.1	46.7
years_16_20	473	13.3	13.5	59.3	60.2
years_21_more	1392	39.1	39.7	98.5	99.9
Scantron_Error	5	0.1	0.1	98.6	100.0
NA	49	1.4	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

	n	%	val%	%cum	val%cum
Less_than_1_year	18	5.6	5.6	5.6	5.6
years_1_5	67	20.9	20.9	26.5	26.5
years_6_10	39	12.1	12.1	38.6	38.6
years_11_15	26	8.1	8.1	46.7	46.7
years_16_20	40	12.5	12.5	59.2	59.2
years_21_more	131	40.8	40.8	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	321	100.0	100.0	100.0	100.0

Northern CA

	n	%	val%	%cum	val%cum
Less_than_1_year	12	5.7	5.8	5.7	5.8
years_1_5	36	17.1	17.5	22.9	23.3
years_6_10	29	13.8	14.1	36.7	37.4
years_11_15	25	11.9	12.1	48.6	49.5
years_16_20	21	10.0	10.2	58.6	59.7
years_21_more	82	39.0	39.8	97.6	99.5
Scantron_Error	1	0.5	0.5	98.1	100.0
NA	4	1.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

	n	%	val%	%cum	val%cum
Less_than_1_year	11	3.5	3.6	3.5	3.6
years_1_5	74	23.5	24.1	27.0	27.7
years_6_10	53	16.8	17.3	43.8	45.0
years_11_15	43	13.7	14.0	57.5	59.0
years_16_20	46	14.6	15.0	72.1	73.9
years_21_more	79	25.1	25.7	97.1	99.7
Scantron_Error	1	0.3	0.3	97.5	100.0
NA	8	2.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

	n	%	val%	%cum	val%cum
Less_than_1_year	18	5.1	5.1	5.1	5.1
years_1_5	67	18.8	18.9	23.9	23.9
years_6_10	57	16.0	16.1	39.9	40.0
years_11_15	48	13.5	13.5	53.4	53.5
years_16_20	38	10.7	10.7	64.0	64.2
years_21_more	126	35.4	35.5	99.4	99.7
Scantron_Error	1	0.3	0.3	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

	n	%	val%	%cum	val%cum
Less_than_1_year	16	2.7	2.8	2.7	2.8
years_1_5	75	12.8	13.0	15.6	15.8
years_6_10	69	11.8	12.0	27.4	27.7
years_11_15	72	12.3	12.5	39.7	40.2
years_16_20	60	10.3	10.4	49.9	50.6
years_21_more	284	48.5	49.2	98.5	99.8
Scantron_Error	1	0.2	0.2	98.6	100.0
NA	8	1.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

	n	%	val%	%cum	val%cum
Less_than_1_year	62	3.5	3.6	3.5	3.6
years_1_5	289	16.5	16.7	20.0	20.3
years_6_10	227	12.9	13.2	33.0	33.5
years_11_15	196	11.2	11.4	44.1	44.8
years_16_20	266	15.2	15.4	59.3	60.3
years_21_more	685	39.1	39.7	98.3	99.9
Scantron_Error	1	0.1	0.1	98.4	100.0
NA	28	1.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

	n	%	val%	%cum	val%cum
Less_than_1_year	0	0.0	0.0	0.0	0.0
years_1_5	2	12.5	12.5	12.5	12.5
years_6_10	4	25.0	25.0	37.5	37.5
years_11_15	3	18.8	18.8	56.2	56.2
years_16_20	2	12.5	12.5	68.8	68.8
years_21_more	5	31.2	31.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

C2A

1. On average, I felt/feel safe walking in my neighborhood day or night.
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis)
- 1. Childhood or young adult life (up to age 30)
  - 1=Strongly Agree
  - 2=Agree
  - 3=Neutral (neither agree nor disagree)
  - 4=Disagree
  - 5=Strongly Disagree

All data

  c2a1 <- as.factor(d[,"c2a1"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	1637	46.0	47.0	46.0	47.0
Agree	1174	33.0	33.7	79.0	80.8
Neutral	431	12.1	12.4	91.1	93.2
Disagree	176	4.9	5.1	96.1	98.2
Strongly_Disagree	56	1.6	1.6	97.7	99.8
Scantron_Error	6	0.2	0.2	97.8	100.0
NA	77	2.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	114	35.5	36.4	35.5	36.4
Agree	131	40.8	41.9	76.3	78.3
Neutral	45	14.0	14.4	90.3	92.7
Disagree	20	6.2	6.4	96.6	99.0
Strongly_Disagree	3	0.9	1.0	97.5	100.0
Scantron_Error	0	0.0	0.0	97.5	100.0
NA	8	2.5	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	109	51.9	52.7	51.9	52.7
Agree	60	28.6	29.0	80.5	81.6
Neutral	29	13.8	14.0	94.3	95.7
Disagree	9	4.3	4.3	98.6	100.0
Strongly_Disagree	0	0.0	0.0	98.6	100.0
Scantron_Error	0	0.0	0.0	98.6	100.0
NA	3	1.4	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	158	50.2	51.6	50.2	51.6
Agree	98	31.1	32.0	81.3	83.7
Neutral	28	8.9	9.2	90.2	92.8
Disagree	16	5.1	5.2	95.2	98.0
Strongly_Disagree	5	1.6	1.6	96.8	99.7
Scantron_Error	1	0.3	0.3	97.1	100.0
NA	9	2.9	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	130	36.5	37.2	36.5	37.2
Agree	117	32.9	33.5	69.4	70.8
Neutral	68	19.1	19.5	88.5	90.3
Disagree	21	5.9	6.0	94.4	96.3
Strongly_Disagree	13	3.7	3.7	98.0	100.0
Scantron_Error	0	0.0	0.0	98.0	100.0
NA	7	2.0	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	252	43.1	44.0	43.1	44.0
Agree	196	33.5	34.2	76.6	78.2
Neutral	77	13.2	13.4	89.7	91.6
Disagree	35	6.0	6.1	95.7	97.7
Strongly_Disagree	12	2.1	2.1	97.8	99.8
Scantron_Error	1	0.2	0.2	97.9	100.0
NA	12	2.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	869	49.5	50.6	49.5	50.6
Agree	563	32.1	32.8	81.6	83.4
Neutral	183	10.4	10.7	92.1	94.1
Disagree	75	4.3	4.4	96.4	98.5
Strongly_Disagree	22	1.3	1.3	97.6	99.8
Scantron_Error	4	0.2	0.2	97.8	100.0
NA	38	2.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	5	31.2	31.2	31.2	31.2
Agree	9	56.2	56.2	87.5	87.5
Neutral	1	6.2	6.2	93.8	93.8
Disagree	0	0.0	0.0	93.8	93.8
Strongly_Disagree	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

C2B

1. Violence was/is not a problem in my neighborhood.
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis)
- 1. Childhood or young adult life (up to age 30)
  - 1=Strongly Agree
  - 2=Agree
  - 3=Neutral (neither agree nor disagree)
  - 4=Disagree
  - 5=Strongly Disagree

All data

  c2b1 <- as.factor(d[,"c2b1"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	1399	39.3	40.6	39.3	40.6
Agree	1094	30.8	31.7	70.1	72.3
Neutral	523	14.7	15.2	84.8	87.5
Disagree	299	8.4	8.7	93.2	96.1
Strongly_Disagree	129	3.6	3.7	96.8	99.9
Scantron_Error	4	0.1	0.1	96.9	100.0
NA	109	3.1	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	89	27.7	28.6	27.7	28.6
Agree	106	33.0	34.1	60.7	62.7
Neutral	63	19.6	20.3	80.4	83.0
Disagree	42	13.1	13.5	93.5	96.5
Strongly_Disagree	11	3.4	3.5	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	10	3.1	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	92	43.8	44.4	43.8	44.4
Agree	55	26.2	26.6	70.0	71.0
Neutral	38	18.1	18.4	88.1	89.4
Disagree	14	6.7	6.8	94.8	96.1
Strongly_Disagree	8	3.8	3.9	98.6	100.0
Scantron_Error	0	0.0	0.0	98.6	100.0
NA	3	1.4	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	140	44.4	46.1	44.4	46.1
Agree	92	29.2	30.3	73.7	76.3
Neutral	39	12.4	12.8	86.0	89.1
Disagree	23	7.3	7.6	93.3	96.7
Strongly_Disagree	10	3.2	3.3	96.5	100.0
Scantron_Error	0	0.0	0.0	96.5	100.0
NA	11	3.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	96	27.0	28.0	27.0	28.0
Agree	93	26.1	27.1	53.1	55.1
Neutral	96	27.0	28.0	80.1	83.1
Disagree	39	11.0	11.4	91.0	94.5
Strongly_Disagree	19	5.3	5.5	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	13	3.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	194	33.2	34.5	33.2	34.5
Agree	192	32.8	34.1	66.0	68.6
Neutral	76	13.0	13.5	79.0	82.1
Disagree	69	11.8	12.3	90.8	94.3
Strongly_Disagree	30	5.1	5.3	95.9	99.6
Scantron_Error	2	0.3	0.4	96.2	100.0
NA	22	3.8	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	783	44.6	46.0	44.6	46.0
Agree	551	31.4	32.3	76.1	78.3
Neutral	209	11.9	12.3	88.0	90.6
Disagree	109	6.2	6.4	94.2	96.9
Strongly_Disagree	50	2.9	2.9	97.0	99.9
Scantron_Error	2	0.1	0.1	97.1	100.0
NA	50	2.9	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	5	31.2	31.2	31.2	31.2
Agree	5	31.2	31.2	62.5	62.5
Neutral	2	12.5	12.5	75.0	75.0
Disagree	3	18.8	18.8	93.8	93.8
Strongly_Disagree	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

C2C

1. My neighborhood was/is safe from crime.
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis)
- 1. Childhood or young adult life (up to age 30)
  - 1=Strongly Agree
  - 2=Agree
  - 3=Neutral (neither agree nor disagree)
  - 4=Disagree
  - 5=Strongly Disagree

All data

  c2c1 <- as.factor(d[,"c2c1"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5",
                     Scantron_Error="*")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	1026	28.8	29.9	28.8	29.9
Agree	1073	30.2	31.2	59.0	61.1
Neutral	772	21.7	22.5	80.7	83.6
Disagree	436	12.3	12.7	93.0	96.3
Strongly_Disagree	123	3.5	3.6	96.4	99.9
Scantron_Error	5	0.1	0.1	96.6	100.0
NA	122	3.4	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	3557	100	NA	100	NA
Total	3557	100	100	100	100

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	68	21.2	21.9	21.2	21.9
Agree	89	27.7	28.6	48.9	50.5
Neutral	81	25.2	26.0	74.1	76.5
Disagree	62	19.3	19.9	93.5	96.5
Strongly_Disagree	11	3.4	3.5	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	10	3.1	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	63	30.0	30.9	30.0	30.9
Agree	59	28.1	28.9	58.1	59.8
Neutral	46	21.9	22.5	80.0	82.4
Disagree	28	13.3	13.7	93.3	96.1
Strongly_Disagree	8	3.8	3.9	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	6	2.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	84	26.7	27.8	26.7	27.8
Agree	102	32.4	33.8	59.0	61.6
Neutral	76	24.1	25.2	83.2	86.8
Disagree	32	10.2	10.6	93.3	97.4
Strongly_Disagree	8	2.5	2.6	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	69	19.4	20.0	19.4	20.0
Agree	78	21.9	22.6	41.3	42.6
Neutral	112	31.5	32.5	72.8	75.1
Disagree	63	17.7	18.3	90.4	93.3
Strongly_Disagree	23	6.5	6.7	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	11	3.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	156	26.7	27.6	26.7	27.6
Agree	180	30.8	31.9	57.4	59.5
Neutral	122	20.9	21.6	78.3	81.1
Disagree	77	13.2	13.6	91.5	94.7
Strongly_Disagree	28	4.8	5.0	96.2	99.6
Scantron_Error	2	0.3	0.4	96.6	100.0
NA	20	3.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	580	33.1	34.3	33.1	34.3
Agree	561	32.0	33.2	65.1	67.4
Neutral	330	18.8	19.5	83.9	86.9
Disagree	173	9.9	10.2	93.7	97.2
Strongly_Disagree	45	2.6	2.7	96.3	99.8
Scantron_Error	3	0.2	0.2	96.5	100.0
NA	62	3.5	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	1754	100	NA	100	NA
Total	1754	100	100	100	100

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Strongly_Agree	6	37.5	37.5	37.5	37.5
Agree	4	25.0	25.0	62.5	62.5
Neutral	5	31.2	31.2	93.8	93.8
Disagree	1	6.2	6.2	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Strongly_Agree	0	0	NaN	0	NaN
Agree	0	0	NaN	0	NaN
Neutral	0	0	NaN	0	NaN
Disagree	0	0	NaN	0	NaN
Strongly_Disagree	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	16	100	NA	100	NA
Total	16	100	100	100	100

C3A

C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
1. Traffic
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Non/Minor problem
  - 2=Somewhat serious problem
  - 3=Very serious problem
  - 88=Don’t Know

All data

  c3a1 <- as.factor(d[,"c3a1"])
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	2430	68.3	70.3	68.3	70.3
Somewhat_serious	642	18.0	18.6	86.4	88.8
Very_serious	245	6.9	7.1	93.3	95.9
Dont_know	139	3.9	4.0	97.2	99.9
Scantron_Error	3	0.1	0.1	97.2	100.0
NA	98	2.8	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	2310	64.9	69.3	64.9	69.3
Somewhat_serious	705	19.8	21.1	84.8	90.4
Very_serious	141	4.0	4.2	88.7	94.6
Dont_know	176	4.9	5.3	93.7	99.9
Scantron_Error	3	0.1	0.1	93.8	100.0
NA	222	6.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	2551	71.7	77.2	71.7	77.2
Somewhat_serious	397	11.2	12.0	82.9	89.2
Very_serious	90	2.5	2.7	85.4	91.9
Dont_know	264	7.4	8.0	92.8	99.9
Scantron_Error	3	0.1	0.1	92.9	100.0
NA	252	7.1	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	161	50.2	51.1	50.2	51.1
Somewhat_serious	96	29.9	30.5	80.1	81.6
Very_serious	53	16.5	16.8	96.6	98.4
Dont_know	5	1.6	1.6	98.1	100.0
Scantron_Error	0	0.0	0.0	98.1	100.0
NA	6	1.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	163	50.8	52.2	50.8	52.2
Somewhat_serious	116	36.1	37.2	86.9	89.4
Very_serious	24	7.5	7.7	94.4	97.1
Dont_know	9	2.8	2.9	97.2	100.0
Scantron_Error	0	0.0	0.0	97.2	100.0
NA	9	2.8	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	235	73.2	75.8	73.2	75.8
Somewhat_serious	44	13.7	14.2	86.9	90.0
Very_serious	14	4.4	4.5	91.3	94.5
Dont_know	17	5.3	5.5	96.6	100.0
Scantron_Error	0	0.0	0.0	96.6	100.0
NA	11	3.4	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	124	59.0	60.5	59.0	60.5
Somewhat_serious	59	28.1	28.8	87.1	89.3
Very_serious	19	9.0	9.3	96.2	98.5
Dont_know	3	1.4	1.5	97.6	100.0
Scantron_Error	0	0.0	0.0	97.6	100.0
NA	5	2.4	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	128	61.0	63.4	61.0	63.4
Somewhat_serious	54	25.7	26.7	86.7	90.1
Very_serious	13	6.2	6.4	92.9	96.5
Dont_know	7	3.3	3.5	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	8	3.8	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	149	71.0	74.9	71.0	74.9
Somewhat_serious	30	14.3	15.1	85.2	89.9
Very_serious	6	2.9	3.0	88.1	93.0
Dont_know	14	6.7	7.0	94.8	100.0
Scantron_Error	0	0.0	0.0	94.8	100.0
NA	11	5.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	217	68.9	71.1	68.9	71.1
Somewhat_serious	60	19.0	19.7	87.9	90.8
Very_serious	21	6.7	6.9	94.6	97.7
Dont_know	7	2.2	2.3	96.8	100.0
Scantron_Error	0	0.0	0.0	96.8	100.0
NA	10	3.2	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	195	61.9	65.4	61.9	65.4
Somewhat_serious	66	21.0	22.1	82.9	87.6
Very_serious	22	7.0	7.4	89.8	95.0
Dont_know	15	4.8	5.0	94.6	100.0
Scantron_Error	0	0.0	0.0	94.6	100.0
NA	17	5.4	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	208	66.0	70.0	66.0	70.0
Somewhat_serious	53	16.8	17.8	82.9	87.9
Very_serious	15	4.8	5.1	87.6	92.9
Dont_know	21	6.7	7.1	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	18	5.7	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	238	66.9	69.6	66.9	69.6
Somewhat_serious	67	18.8	19.6	85.7	89.2
Very_serious	19	5.3	5.6	91.0	94.7
Dont_know	18	5.1	5.3	96.1	100.0
Scantron_Error	0	0.0	0.0	96.1	100.0
NA	14	3.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	219	61.5	67.6	61.5	67.6
Somewhat_serious	82	23.0	25.3	84.6	92.9
Very_serious	8	2.2	2.5	86.8	95.4
Dont_know	15	4.2	4.6	91.0	100.0
Scantron_Error	0	0.0	0.0	91.0	100.0
NA	32	9.0	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	238	66.9	74.1	66.9	74.1
Somewhat_serious	48	13.5	15.0	80.3	89.1
Very_serious	7	2.0	2.2	82.3	91.3
Dont_know	28	7.9	8.7	90.2	100.0
Scantron_Error	0	0.0	0.0	90.2	100.0
NA	35	9.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	434	74.2	76.1	74.2	76.1
Somewhat_serious	75	12.8	13.2	87.0	89.3
Very_serious	26	4.4	4.6	91.5	93.9
Dont_know	34	5.8	6.0	97.3	99.8
Scantron_Error	1	0.2	0.2	97.4	100.0
NA	15	2.6	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	400	68.4	74.3	68.4	74.3
Somewhat_serious	91	15.6	16.9	83.9	91.3
Very_serious	15	2.6	2.8	86.5	94.1
Dont_know	31	5.3	5.8	91.8	99.8
Scantron_Error	1	0.2	0.2	92.0	100.0
NA	47	8.0	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	422	72.1	78.7	72.1	78.7
Somewhat_serious	54	9.2	10.1	81.4	88.8
Very_serious	14	2.4	2.6	83.8	91.4
Dont_know	45	7.7	8.4	91.5	99.8
Scantron_Error	1	0.2	0.2	91.6	100.0
NA	49	8.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	1245	71.0	73.0	71.0	73.0
Somewhat_serious	281	16.0	16.5	87.0	89.4
Very_serious	107	6.1	6.3	93.1	95.7
Dont_know	71	4.0	4.2	97.1	99.9
Scantron_Error	2	0.1	0.1	97.3	100.0
NA	48	2.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	1194	68.1	72.5	68.1	72.5
Somewhat_serious	294	16.8	17.9	84.8	90.4
Very_serious	59	3.4	3.6	88.2	94.0
Dont_know	97	5.5	5.9	93.7	99.9
Scantron_Error	2	0.1	0.1	93.8	100.0
NA	108	6.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	1287	73.4	79.1	73.4	79.1
Somewhat_serious	167	9.5	10.3	82.9	89.4
Very_serious	34	1.9	2.1	84.8	91.5
Dont_know	137	7.8	8.4	92.6	99.9
Scantron_Error	2	0.1	0.1	92.8	100.0
NA	127	7.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	11	68.8	68.8	68.8	68.8
Somewhat_serious	4	25.0	25.0	93.8	93.8
Very_serious	0	0.0	0.0	93.8	93.8
Dont_know	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	11	68.8	73.3	68.8	73.3
Somewhat_serious	2	12.5	13.3	81.2	86.7
Very_serious	0	0.0	0.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	12	75.0	80.0	75.0	80.0
Somewhat_serious	1	6.2	6.7	81.2	86.7
Very_serious	0	0.0	0.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C3B

C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
1. A lot of noise
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Non/Minor problem
  - 2=Somewhat serious problem
  - 3=Very serious problem
  - 88=Don’t Know

All data

  c3b1 <- as.factor(d[,"c3b1"])
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	2816	79.2	82.1	79.2	82.1
Somewhat_serious	435	12.2	12.7	91.4	94.8
Very_serious	99	2.8	2.9	94.2	97.6
Dont_know	79	2.2	2.3	96.4	99.9
Scantron_Error	2	0.1	0.1	96.5	100.0
NA	126	3.5	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	2541	71.4	77.0	71.4	77.0
Somewhat_serious	538	15.1	16.3	86.6	93.3
Very_serious	94	2.6	2.8	89.2	96.2
Dont_know	124	3.5	3.8	92.7	99.9
Scantron_Error	3	0.1	0.1	92.8	100.0
NA	257	7.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	2531	71.2	77.1	71.2	77.1
Somewhat_serious	437	12.3	13.3	83.4	90.4
Very_serious	107	3.0	3.3	86.4	93.6
Dont_know	207	5.8	6.3	92.3	99.9
Scantron_Error	2	0.1	0.1	92.3	100.0
NA	273	7.7	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	229	71.3	73.4	71.3	73.4
Somewhat_serious	61	19.0	19.6	90.3	92.9
Very_serious	17	5.3	5.4	95.6	98.4
Dont_know	5	1.6	1.6	97.2	100.0
Scantron_Error	0	0.0	0.0	97.2	100.0
NA	9	2.8	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	213	66.4	68.7	66.4	68.7
Somewhat_serious	73	22.7	23.5	89.1	92.3
Very_serious	14	4.4	4.5	93.5	96.8
Dont_know	10	3.1	3.2	96.6	100.0
Scantron_Error	0	0.0	0.0	96.6	100.0
NA	11	3.4	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	235	73.2	76.1	73.2	76.1
Somewhat_serious	45	14.0	14.6	87.2	90.6
Very_serious	12	3.7	3.9	91.0	94.5
Dont_know	17	5.3	5.5	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	12	3.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	158	75.2	77.5	75.2	77.5
Somewhat_serious	37	17.6	18.1	92.9	95.6
Very_serious	8	3.8	3.9	96.7	99.5
Dont_know	1	0.5	0.5	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	6	2.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	145	69.0	72.1	69.0	72.1
Somewhat_serious	39	18.6	19.4	87.6	91.5
Very_serious	11	5.2	5.5	92.9	97.0
Dont_know	6	2.9	3.0	95.7	100.0
Scantron_Error	0	0.0	0.0	95.7	100.0
NA	9	4.3	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	153	72.9	77.3	72.9	77.3
Somewhat_serious	27	12.9	13.6	85.7	90.9
Very_serious	7	3.3	3.5	89.0	94.4
Dont_know	11	5.2	5.6	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	12	5.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	258	81.9	84.6	81.9	84.6
Somewhat_serious	33	10.5	10.8	92.4	95.4
Very_serious	9	2.9	3.0	95.2	98.4
Dont_know	5	1.6	1.6	96.8	100.0
Scantron_Error	0	0.0	0.0	96.8	100.0
NA	10	3.2	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	229	72.7	77.1	72.7	77.1
Somewhat_serious	47	14.9	15.8	87.6	92.9
Very_serious	8	2.5	2.7	90.2	95.6
Dont_know	13	4.1	4.4	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	18	5.7	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	209	66.3	70.4	66.3	70.4
Somewhat_serious	54	17.1	18.2	83.5	88.6
Very_serious	13	4.1	4.4	87.6	92.9
Dont_know	21	6.7	7.1	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	18	5.7	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	261	73.3	77.0	73.3	77.0
Somewhat_serious	55	15.4	16.2	88.8	93.2
Very_serious	14	3.9	4.1	92.7	97.3
Dont_know	9	2.5	2.7	95.2	100.0
Scantron_Error	0	0.0	0.0	95.2	100.0
NA	17	4.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	233	65.4	71.5	65.4	71.5
Somewhat_serious	63	17.7	19.3	83.1	90.8
Very_serious	15	4.2	4.6	87.4	95.4
Dont_know	15	4.2	4.6	91.6	100.0
Scantron_Error	0	0.0	0.0	91.6	100.0
NA	30	8.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	230	64.6	71.2	64.6	71.2
Somewhat_serious	59	16.6	18.3	81.2	89.5
Very_serious	9	2.5	2.8	83.7	92.3
Dont_know	25	7.0	7.7	90.7	100.0
Scantron_Error	0	0.0	0.0	90.7	100.0
NA	33	9.3	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	456	77.9	80.4	77.9	80.4
Somewhat_serious	69	11.8	12.2	89.7	92.6
Very_serious	21	3.6	3.7	93.3	96.3
Dont_know	20	3.4	3.5	96.8	99.8
Scantron_Error	1	0.2	0.2	96.9	100.0
NA	18	3.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	398	68.0	75.5	68.0	75.5
Somewhat_serious	89	15.2	16.9	83.2	92.4
Very_serious	13	2.2	2.5	85.5	94.9
Dont_know	25	4.3	4.7	89.7	99.6
Scantron_Error	2	0.3	0.4	90.1	100.0
NA	58	9.9	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	407	69.6	76.9	69.6	76.9
Somewhat_serious	64	10.9	12.1	80.5	89.0
Very_serious	19	3.2	3.6	83.8	92.6
Dont_know	38	6.5	7.2	90.3	99.8
Scantron_Error	1	0.2	0.2	90.4	100.0
NA	56	9.6	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	1443	82.3	85.5	82.3	85.5
Somewhat_serious	176	10.0	10.4	92.3	95.9
Very_serious	29	1.7	1.7	94.0	97.6
Dont_know	39	2.2	2.3	96.2	99.9
Scantron_Error	1	0.1	0.1	96.2	100.0
NA	66	3.8	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	1311	74.7	80.7	74.7	80.7
Somewhat_serious	225	12.8	13.9	87.6	94.6
Very_serious	32	1.8	2.0	89.4	96.6
Dont_know	55	3.1	3.4	92.5	99.9
Scantron_Error	1	0.1	0.1	92.6	100.0
NA	130	7.4	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	1285	73.3	79.7	73.3	79.7
Somewhat_serious	185	10.5	11.5	83.8	91.1
Very_serious	47	2.7	2.9	86.5	94.0
Dont_know	95	5.4	5.9	91.9	99.9
Scantron_Error	1	0.1	0.1	92.0	100.0
NA	141	8.0	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	11	68.8	68.8	68.8	68.8
Somewhat_serious	4	25.0	25.0	93.8	93.8
Very_serious	1	6.2	6.2	100.0	100.0
Dont_know	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	12	75.0	80.0	75.0	80.0
Somewhat_serious	2	12.5	13.3	87.5	93.3
Very_serious	1	6.2	6.7	93.8	100.0
Dont_know	0	0.0	0.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	12	75.0	80	75.0	80
Somewhat_serious	3	18.8	20	93.8	100
Very_serious	0	0.0	0	93.8	100
Dont_know	0	0.0	0	93.8	100
Scantron_Error	0	0.0	0	93.8	100
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100	100.0	100

C3C

C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
1. Trash and litter
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Non/Minor problem
  - 2=Somewhat serious problem
  - 3=Very serious problem
  - 88=Don’t Know

All data

  c3c1 <- as.factor(d[,"c3c1"])
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	2832	79.6	82.6	79.6	82.6
Somewhat_serious	371	10.4	10.8	90.0	93.4
Very_serious	152	4.3	4.4	94.3	97.8
Dont_know	73	2.1	2.1	96.4	99.9
Scantron_Error	2	0.1	0.1	96.4	100.0
NA	127	3.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	2637	74.1	79.9	74.1	79.9
Somewhat_serious	453	12.7	13.7	86.9	93.6
Very_serious	103	2.9	3.1	89.8	96.7
Dont_know	107	3.0	3.2	92.8	99.9
Scantron_Error	2	0.1	0.1	92.8	100.0
NA	255	7.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	2561	72.0	77.9	72.0	77.9
Somewhat_serious	430	12.1	13.1	84.1	91.0
Very_serious	121	3.4	3.7	87.5	94.7
Dont_know	172	4.8	5.2	92.3	99.9
Scantron_Error	2	0.1	0.1	92.4	100.0
NA	271	7.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	235	73.2	75.3	73.2	75.3
Somewhat_serious	53	16.5	17.0	89.7	92.3
Very_serious	21	6.5	6.7	96.3	99.0
Dont_know	3	0.9	1.0	97.2	100.0
Scantron_Error	0	0.0	0.0	97.2	100.0
NA	9	2.8	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	234	72.9	76.2	72.9	76.2
Somewhat_serious	57	17.8	18.6	90.7	94.8
Very_serious	11	3.4	3.6	94.1	98.4
Dont_know	5	1.6	1.6	95.6	100.0
Scantron_Error	0	0.0	0.0	95.6	100.0
NA	14	4.4	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	253	78.8	81.9	78.8	81.9
Somewhat_serious	38	11.8	12.3	90.7	94.2
Very_serious	9	2.8	2.9	93.5	97.1
Dont_know	9	2.8	2.9	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	12	3.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	157	74.8	77.0	74.8	77.0
Somewhat_serious	27	12.9	13.2	87.6	90.2
Very_serious	17	8.1	8.3	95.7	98.5
Dont_know	3	1.4	1.5	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	6	2.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	156	74.3	77.6	74.3	77.6
Somewhat_serious	29	13.8	14.4	88.1	92.0
Very_serious	10	4.8	5.0	92.9	97.0
Dont_know	6	2.9	3.0	95.7	100.0
Scantron_Error	0	0.0	0.0	95.7	100.0
NA	9	4.3	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	148	70.5	75.1	70.5	75.1
Somewhat_serious	30	14.3	15.2	84.8	90.4
Very_serious	9	4.3	4.6	89.0	94.9
Dont_know	10	4.8	5.1	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	13	6.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	260	82.5	85.8	82.5	85.8
Somewhat_serious	25	7.9	8.3	90.5	94.1
Very_serious	15	4.8	5.0	95.2	99.0
Dont_know	3	1.0	1.0	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	234	74.3	79.1	74.3	79.1
Somewhat_serious	40	12.7	13.5	87.0	92.6
Very_serious	12	3.8	4.1	90.8	96.6
Dont_know	10	3.2	3.4	94.0	100.0
Scantron_Error	0	0.0	0.0	94.0	100.0
NA	19	6.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	210	66.7	70.9	66.7	70.9
Somewhat_serious	46	14.6	15.5	81.3	86.5
Very_serious	22	7.0	7.4	88.3	93.9
Dont_know	18	5.7	6.1	94.0	100.0
Scantron_Error	0	0.0	0.0	94.0	100.0
NA	19	6.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	260	73.0	75.8	73.0	75.8
Somewhat_serious	49	13.8	14.3	86.8	90.1
Very_serious	23	6.5	6.7	93.3	96.8
Dont_know	11	3.1	3.2	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	13	3.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	226	63.5	68.9	63.5	68.9
Somewhat_serious	69	19.4	21.0	82.9	89.9
Very_serious	23	6.5	7.0	89.3	97.0
Dont_know	10	2.8	3.0	92.1	100.0
Scantron_Error	0	0.0	0.0	92.1	100.0
NA	28	7.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	226	63.5	69.3	63.5	69.3
Somewhat_serious	68	19.1	20.9	82.6	90.2
Very_serious	14	3.9	4.3	86.5	94.5
Dont_know	18	5.1	5.5	91.6	100.0
Scantron_Error	0	0.0	0.0	91.6	100.0
NA	30	8.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	447	76.4	79.7	76.4	79.7
Somewhat_serious	66	11.3	11.8	87.7	91.4
Very_serious	28	4.8	5.0	92.5	96.4
Dont_know	19	3.2	3.4	95.7	99.8
Scantron_Error	1	0.2	0.2	95.9	100.0
NA	24	4.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	407	69.6	76.6	69.6	76.6
Somewhat_serious	85	14.5	16.0	84.1	92.7
Very_serious	15	2.6	2.8	86.7	95.5
Dont_know	23	3.9	4.3	90.6	99.8
Scantron_Error	1	0.2	0.2	90.8	100.0
NA	54	9.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	404	69.1	76.7	69.1	76.7
Somewhat_serious	72	12.3	13.7	81.4	90.3
Very_serious	18	3.1	3.4	84.4	93.7
Dont_know	32	5.5	6.1	89.9	99.8
Scantron_Error	1	0.2	0.2	90.1	100.0
NA	58	9.9	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	1460	83.2	86.3	83.2	86.3
Somewhat_serious	149	8.5	8.8	91.7	95.2
Very_serious	48	2.7	2.8	94.5	98.0
Dont_know	33	1.9	2.0	96.4	99.9
Scantron_Error	1	0.1	0.1	96.4	100.0
NA	63	3.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	1368	78.0	84.2	78.0	84.2
Somewhat_serious	171	9.7	10.5	87.7	94.8
Very_serious	32	1.8	2.0	89.6	96.7
Dont_know	52	3.0	3.2	92.5	99.9
Scantron_Error	1	0.1	0.1	92.6	100.0
NA	130	7.4	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	1308	74.6	80.9	74.6	80.9
Somewhat_serious	174	9.9	10.8	84.5	91.7
Very_serious	49	2.8	3.0	87.3	94.7
Dont_know	84	4.8	5.2	92.1	99.9
Scantron_Error	1	0.1	0.1	92.1	100.0
NA	138	7.9	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	13	81.2	81.2	81.2	81.2
Somewhat_serious	2	12.5	12.5	93.8	93.8
Very_serious	0	0.0	0.0	93.8	93.8
Dont_know	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	12	75.0	80.0	75.0	80.0
Somewhat_serious	2	12.5	13.3	87.5	93.3
Very_serious	0	0.0	0.0	87.5	93.3
Dont_know	1	6.2	6.7	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	12	75.0	80.0	75.0	80.0
Somewhat_serious	2	12.5	13.3	87.5	93.3
Very_serious	0	0.0	0.0	87.5	93.3
Dont_know	1	6.2	6.7	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C3D

C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
1. Too much light at night
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Non/Minor problem
  - 2=Somewhat serious problem
  - 3=Very serious problem
  - 88=Don’t Know

All data

  c3d1 <- as.factor(d[,"c3d1"])
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	3139	88.2	91.8	88.2	91.8
Somewhat_serious	114	3.2	3.3	91.5	95.1
Very_serious	33	0.9	1.0	92.4	96.1
Dont_know	131	3.7	3.8	96.1	99.9
Scantron_Error	2	0.1	0.1	96.1	100.0
NA	138	3.9	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	2927	82.3	89.0	82.3	89.0
Somewhat_serious	179	5.0	5.4	87.3	94.4
Very_serious	29	0.8	0.9	88.1	95.3
Dont_know	153	4.3	4.7	92.4	99.9
Scantron_Error	2	0.1	0.1	92.5	100.0
NA	267	7.5	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	2857	80.3	87.0	80.3	87.0
Somewhat_serious	153	4.3	4.7	84.6	91.7
Very_serious	35	1.0	1.1	85.6	92.8
Dont_know	236	6.6	7.2	92.2	99.9
Scantron_Error	2	0.1	0.1	92.3	100.0
NA	274	7.7	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	289	90.0	92.9	90.0	92.9
Somewhat_serious	13	4.0	4.2	94.1	97.1
Very_serious	3	0.9	1.0	95.0	98.1
Dont_know	6	1.9	1.9	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	10	3.1	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	283	88.2	91.0	88.2	91.0
Somewhat_serious	21	6.5	6.8	94.7	97.7
Very_serious	0	0.0	0.0	94.7	97.7
Dont_know	7	2.2	2.3	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	10	3.1	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	278	86.6	90.0	86.6	90.0
Somewhat_serious	14	4.4	4.5	91.0	94.5
Very_serious	1	0.3	0.3	91.3	94.8
Dont_know	16	5.0	5.2	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	12	3.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	188	89.5	92.2	89.5	92.2
Somewhat_serious	10	4.8	4.9	94.3	97.1
Very_serious	0	0.0	0.0	94.3	97.1
Dont_know	6	2.9	2.9	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	6	2.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	176	83.8	88.0	83.8	88.0
Somewhat_serious	15	7.1	7.5	91.0	95.5
Very_serious	1	0.5	0.5	91.4	96.0
Dont_know	8	3.8	4.0	95.2	100.0
Scantron_Error	0	0.0	0.0	95.2	100.0
NA	10	4.8	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	177	84.3	89.4	84.3	89.4
Somewhat_serious	6	2.9	3.0	87.1	92.4
Very_serious	3	1.4	1.5	88.6	93.9
Dont_know	12	5.7	6.1	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	12	5.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	277	87.9	91.4	87.9	91.4
Somewhat_serious	8	2.5	2.6	90.5	94.1
Very_serious	5	1.6	1.7	92.1	95.7
Dont_know	13	4.1	4.3	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	261	82.9	88.5	82.9	88.5
Somewhat_serious	16	5.1	5.4	87.9	93.9
Very_serious	3	1.0	1.0	88.9	94.9
Dont_know	15	4.8	5.1	93.7	100.0
Scantron_Error	0	0.0	0.0	93.7	100.0
NA	20	6.3	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	249	79.0	84.4	79.0	84.4
Somewhat_serious	17	5.4	5.8	84.4	90.2
Very_serious	5	1.6	1.7	86.0	91.9
Dont_know	24	7.6	8.1	93.7	100.0
Scantron_Error	0	0.0	0.0	93.7	100.0
NA	20	6.3	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	306	86.0	89.5	86.0	89.5
Somewhat_serious	15	4.2	4.4	90.2	93.9
Very_serious	5	1.4	1.5	91.6	95.3
Dont_know	16	4.5	4.7	96.1	100.0
Scantron_Error	0	0.0	0.0	96.1	100.0
NA	14	3.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	278	78.1	85.0	78.1	85.0
Somewhat_serious	27	7.6	8.3	85.7	93.3
Very_serious	6	1.7	1.8	87.4	95.1
Dont_know	16	4.5	4.9	91.9	100.0
Scantron_Error	0	0.0	0.0	91.9	100.0
NA	29	8.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	264	74.2	82.2	74.2	82.2
Somewhat_serious	27	7.6	8.4	81.7	90.7
Very_serious	6	1.7	1.9	83.4	92.5
Dont_know	24	6.7	7.5	90.2	100.0
Scantron_Error	0	0.0	0.0	90.2	100.0
NA	35	9.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	502	85.8	89.6	85.8	89.6
Somewhat_serious	19	3.2	3.4	89.1	93.0
Very_serious	10	1.7	1.8	90.8	94.8
Dont_know	28	4.8	5.0	95.6	99.8
Scantron_Error	1	0.2	0.2	95.7	100.0
NA	25	4.3	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	459	78.5	87.6	78.5	87.6
Somewhat_serious	28	4.8	5.3	83.2	92.9
Very_serious	8	1.4	1.5	84.6	94.5
Dont_know	28	4.8	5.3	89.4	99.8
Scantron_Error	1	0.2	0.2	89.6	100.0
NA	61	10.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	458	78.3	87.2	78.3	87.2
Somewhat_serious	22	3.8	4.2	82.1	91.4
Very_serious	6	1.0	1.1	83.1	92.6
Dont_know	38	6.5	7.2	89.6	99.8
Scantron_Error	1	0.2	0.2	89.7	100.0
NA	60	10.3	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	1564	89.2	92.9	89.2	92.9
Somewhat_serious	49	2.8	2.9	92.0	95.8
Very_serious	10	0.6	0.6	92.5	96.4
Dont_know	60	3.4	3.6	96.0	99.9
Scantron_Error	1	0.1	0.1	96.0	100.0
NA	70	4.0	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	1457	83.1	90.0	83.1	90.0
Somewhat_serious	72	4.1	4.4	87.2	94.4
Very_serious	11	0.6	0.7	87.8	95.1
Dont_know	78	4.4	4.8	92.2	99.9
Scantron_Error	1	0.1	0.1	92.3	100.0
NA	135	7.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	1418	80.8	87.5	80.8	87.5
Somewhat_serious	67	3.8	4.1	84.7	91.6
Very_serious	14	0.8	0.9	85.5	92.5
Dont_know	121	6.9	7.5	92.4	99.9
Scantron_Error	1	0.1	0.1	92.4	100.0
NA	133	7.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Non_Minor	13	81.2	86.7	81.2	86.7
Somewhat_serious	0	0.0	0.0	81.2	86.7
Very_serious	0	0.0	0.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Non_Minor	13	81.2	92.9	81.2	92.9
Somewhat_serious	0	0.0	0.0	81.2	92.9
Very_serious	0	0.0	0.0	81.2	92.9
Dont_know	1	6.2	7.1	87.5	100.0
Scantron_Error	0	0.0	0.0	87.5	100.0
NA	2	12.5	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Non_Minor	13	81.2	92.9	81.2	92.9
Somewhat_serious	0	0.0	0.0	81.2	92.9
Very_serious	0	0.0	0.0	81.2	92.9
Dont_know	1	6.2	7.1	87.5	100.0
Scantron_Error	0	0.0	0.0	87.5	100.0
NA	2	12.5	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C4A

C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Often
  - 2=Sometimes
  - 3=Rarely/Never
  - 88=Don’t Know

All data

  c4a1 <- as.factor(d[,"c4a1"])
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	1283	36.1	37.0	36.1	37.0
Sometimes	1453	40.8	41.9	76.9	78.9
Rarely_Never	672	18.9	19.4	95.8	98.2
Dont_know	61	1.7	1.8	97.5	100.0
Scantron_Error	0	0.0	0.0	97.5	100.0
NA	88	2.5	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	1288	36.2	38.7	36.2	38.7
Sometimes	1437	40.4	43.1	76.6	81.8
Rarely_Never	479	13.5	14.4	90.1	96.2
Dont_know	128	3.6	3.8	93.7	100.0
Scantron_Error	0	0.0	0.0	93.7	100.0
NA	225	6.3	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	1901	53.4	57.6	53.4	57.6
Sometimes	875	24.6	26.5	78.0	84.1
Rarely_Never	313	8.8	9.5	86.8	93.6
Dont_know	211	5.9	6.4	92.8	100.0
Scantron_Error	0	0.0	0.0	92.8	100.0
NA	257	7.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	132	41.1	41.9	41.1	41.9
Sometimes	125	38.9	39.7	80.1	81.6
Rarely_Never	56	17.4	17.8	97.5	99.4
Dont_know	2	0.6	0.6	98.1	100.0
Scantron_Error	0	0.0	0.0	98.1	100.0
NA	6	1.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	125	38.9	40.2	38.9	40.2
Sometimes	137	42.7	44.1	81.6	84.2
Rarely_Never	41	12.8	13.2	94.4	97.4
Dont_know	8	2.5	2.6	96.9	100.0
Scantron_Error	0	0.0	0.0	96.9	100.0
NA	10	3.1	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	200	62.3	64.7	62.3	64.7
Sometimes	75	23.4	24.3	85.7	89.0
Rarely_Never	22	6.9	7.1	92.5	96.1
Dont_know	12	3.7	3.9	96.3	100.0
Scantron_Error	0	0.0	0.0	96.3	100.0
NA	12	3.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	91	43.3	44.6	43.3	44.6
Sometimes	74	35.2	36.3	78.6	80.9
Rarely_Never	37	17.6	18.1	96.2	99.0
Dont_know	2	1.0	1.0	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	6	2.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	91	43.3	45.3	43.3	45.3
Sometimes	81	38.6	40.3	81.9	85.6
Rarely_Never	28	13.3	13.9	95.2	99.5
Dont_know	1	0.5	0.5	95.7	100.0
Scantron_Error	0	0.0	0.0	95.7	100.0
NA	9	4.3	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	124	59.0	62.3	59.0	62.3
Sometimes	54	25.7	27.1	84.8	89.4
Rarely_Never	11	5.2	5.5	90.0	95.0
Dont_know	10	4.8	5.0	94.8	100.0
Scantron_Error	0	0.0	0.0	94.8	100.0
NA	11	5.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	121	38.4	39.8	38.4	39.8
Sometimes	116	36.8	38.2	75.2	78.0
Rarely_Never	63	20.0	20.7	95.2	98.7
Dont_know	4	1.3	1.3	96.5	100.0
Scantron_Error	0	0.0	0.0	96.5	100.0
NA	11	3.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	106	33.7	35.8	33.7	35.8
Sometimes	132	41.9	44.6	75.6	80.4
Rarely_Never	43	13.7	14.5	89.2	94.9
Dont_know	15	4.8	5.1	94.0	100.0
Scantron_Error	0	0.0	0.0	94.0	100.0
NA	19	6.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	168	53.3	57.3	53.3	57.3
Sometimes	72	22.9	24.6	76.2	81.9
Rarely_Never	31	9.8	10.6	86.0	92.5
Dont_know	22	7.0	7.5	93.0	100.0
Scantron_Error	0	0.0	0.0	93.0	100.0
NA	22	7.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	153	43.0	44.7	43.0	44.7
Sometimes	144	40.4	42.1	83.4	86.8
Rarely_Never	38	10.7	11.1	94.1	98.0
Dont_know	7	2.0	2.0	96.1	100.0
Scantron_Error	0	0.0	0.0	96.1	100.0
NA	14	3.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	143	40.2	43.1	40.2	43.1
Sometimes	144	40.4	43.4	80.6	86.4
Rarely_Never	32	9.0	9.6	89.6	96.1
Dont_know	13	3.7	3.9	93.3	100.0
Scantron_Error	0	0.0	0.0	93.3	100.0
NA	24	6.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	196	55.1	60.9	55.1	60.9
Sometimes	87	24.4	27.0	79.5	87.9
Rarely_Never	15	4.2	4.7	83.7	92.5
Dont_know	24	6.7	7.5	90.4	100.0
Scantron_Error	0	0.0	0.0	90.4	100.0
NA	34	9.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	232	39.7	40.5	39.7	40.5
Sometimes	223	38.1	38.9	77.8	79.4
Rarely_Never	102	17.4	17.8	95.2	97.2
Dont_know	16	2.7	2.8	97.9	100.0
Scantron_Error	0	0.0	0.0	97.9	100.0
NA	12	2.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	240	41.0	45.1	41.0	45.1
Sometimes	189	32.3	35.5	73.3	80.6
Rarely_Never	80	13.7	15.0	87.0	95.7
Dont_know	23	3.9	4.3	90.9	100.0
Scantron_Error	0	0.0	0.0	90.9	100.0
NA	53	9.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	310	53.0	58.3	53.0	58.3
Sometimes	131	22.4	24.6	75.4	82.9
Rarely_Never	54	9.2	10.2	84.6	93.0
Dont_know	37	6.3	7.0	90.9	100.0
Scantron_Error	0	0.0	0.0	90.9	100.0
NA	53	9.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	548	31.2	32.0	31.2	32.0
Sometimes	763	43.5	44.5	74.7	76.4
Rarely_Never	374	21.3	21.8	96.1	98.3
Dont_know	30	1.7	1.7	97.8	100.0
Scantron_Error	0	0.0	0.0	97.8	100.0
NA	39	2.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	580	33.1	35.3	33.1	35.3
Sometimes	744	42.4	45.2	75.5	80.5
Rarely_Never	253	14.4	15.4	89.9	95.9
Dont_know	68	3.9	4.1	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	109	6.2	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	895	51.0	54.9	51.0	54.9
Sometimes	451	25.7	27.7	76.7	82.6
Rarely_Never	179	10.2	11.0	86.9	93.6
Dont_know	105	6.0	6.4	92.9	100.0
Scantron_Error	0	0.0	0.0	92.9	100.0
NA	124	7.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	6	37.5	37.5	37.5	37.5
Sometimes	8	50.0	50.0	87.5	87.5
Rarely_Never	2	12.5	12.5	100.0	100.0
Dont_know	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	3	18.8	20.0	18.8	20.0
Sometimes	10	62.5	66.7	81.2	86.7
Rarely_Never	2	12.5	13.3	93.8	100.0
Dont_know	0	0.0	0.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	8	50.0	53.3	50.0	53.3
Sometimes	5	31.2	33.3	81.2	86.7
Rarely_Never	1	6.2	6.7	87.5	93.3
Dont_know	1	6.2	6.7	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C4B

C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Often
  - 2=Sometimes
  - 3=Rarely/Never
  - 88=Don’t Know

All data

  c4b1 <- as.factor(d[,"c4b1"])
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	1604	45.1	47.1	45.1	47.1
Sometimes	1062	29.9	31.2	75.0	78.3
Rarely_Never	498	14.0	14.6	89.0	92.9
Dont_know	241	6.8	7.1	95.7	100.0
Scantron_Error	1	0.0	0.0	95.8	100.0
NA	151	4.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	1381	38.8	42.2	38.8	42.2
Sometimes	1189	33.4	36.3	72.3	78.6
Rarely_Never	467	13.1	14.3	85.4	92.8
Dont_know	231	6.5	7.1	91.9	99.9
Scantron_Error	3	0.1	0.1	92.0	100.0
NA	286	8.0	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	1891	53.2	58.3	53.2	58.3
Sometimes	756	21.3	23.3	74.4	81.6
Rarely_Never	302	8.5	9.3	82.9	91.0
Dont_know	291	8.2	9.0	91.1	99.9
Scantron_Error	2	0.1	0.1	91.1	100.0
NA	315	8.9	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	119	37.1	37.9	37.1	37.9
Sometimes	118	36.8	37.6	73.8	75.5
Rarely_Never	50	15.6	15.9	89.4	91.4
Dont_know	27	8.4	8.6	97.8	100.0
Scantron_Error	0	0.0	0.0	97.8	100.0
NA	7	2.2	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	106	33.0	34.4	33.0	34.4
Sometimes	140	43.6	45.5	76.6	79.9
Rarely_Never	43	13.4	14.0	90.0	93.8
Dont_know	19	5.9	6.2	96.0	100.0
Scantron_Error	0	0.0	0.0	96.0	100.0
NA	13	4.0	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	189	58.9	61.8	58.9	61.8
Sometimes	71	22.1	23.2	81.0	85.0
Rarely_Never	22	6.9	7.2	87.9	92.2
Dont_know	23	7.2	7.5	95.0	99.7
Scantron_Error	1	0.3	0.3	95.3	100.0
NA	15	4.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	93	44.3	46.5	44.3	46.5
Sometimes	57	27.1	28.5	71.4	75.0
Rarely_Never	38	18.1	19.0	89.5	94.0
Dont_know	12	5.7	6.0	95.2	100.0
Scantron_Error	0	0.0	0.0	95.2	100.0
NA	10	4.8	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	83	39.5	42.3	39.5	42.3
Sometimes	64	30.5	32.7	70.0	75.0
Rarely_Never	38	18.1	19.4	88.1	94.4
Dont_know	11	5.2	5.6	93.3	100.0
Scantron_Error	0	0.0	0.0	93.3	100.0
NA	14	6.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	112	53.3	57.4	53.3	57.4
Sometimes	42	20.0	21.5	73.3	79.0
Rarely_Never	25	11.9	12.8	85.2	91.8
Dont_know	16	7.6	8.2	92.9	100.0
Scantron_Error	0	0.0	0.0	92.9	100.0
NA	15	7.1	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	129	41.0	43.1	41.0	43.1
Sometimes	94	29.8	31.4	70.8	74.6
Rarely_Never	50	15.9	16.7	86.7	91.3
Dont_know	26	8.3	8.7	94.9	100.0
Scantron_Error	0	0.0	0.0	94.9	100.0
NA	16	5.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	95	30.2	32.8	30.2	32.8
Sometimes	104	33.0	35.9	63.2	68.6
Rarely_Never	58	18.4	20.0	81.6	88.6
Dont_know	33	10.5	11.4	92.1	100.0
Scantron_Error	0	0.0	0.0	92.1	100.0
NA	25	7.9	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	127	40.3	44.3	40.3	44.3
Sometimes	83	26.3	28.9	66.7	73.2
Rarely_Never	35	11.1	12.2	77.8	85.4
Dont_know	42	13.3	14.6	91.1	100.0
Scantron_Error	0	0.0	0.0	91.1	100.0
NA	28	8.9	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	162	45.5	47.9	45.5	47.9
Sometimes	114	32.0	33.7	77.5	81.7
Rarely_Never	39	11.0	11.5	88.5	93.2
Dont_know	23	6.5	6.8	94.9	100.0
Scantron_Error	0	0.0	0.0	94.9	100.0
NA	18	5.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	143	40.2	43.9	40.2	43.9
Sometimes	127	35.7	39.0	75.8	82.8
Rarely_Never	38	10.7	11.7	86.5	94.5
Dont_know	18	5.1	5.5	91.6	100.0
Scantron_Error	0	0.0	0.0	91.6	100.0
NA	30	8.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	193	54.2	60.7	54.2	60.7
Sometimes	78	21.9	24.5	76.1	85.2
Rarely_Never	22	6.2	6.9	82.3	92.1
Dont_know	25	7.0	7.9	89.3	100.0
Scantron_Error	0	0.0	0.0	89.3	100.0
NA	38	10.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	287	49.1	51.4	49.1	51.4
Sometimes	167	28.5	29.9	77.6	81.4
Rarely_Never	77	13.2	13.8	90.8	95.2
Dont_know	26	4.4	4.7	95.2	99.8
Scantron_Error	1	0.2	0.2	95.4	100.0
NA	27	4.6	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	251	42.9	48.3	42.9	48.3
Sometimes	167	28.5	32.1	71.5	80.4
Rarely_Never	70	12.0	13.5	83.4	93.8
Dont_know	31	5.3	6.0	88.7	99.8
Scantron_Error	1	0.2	0.2	88.9	100.0
NA	65	11.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	313	53.5	60.3	53.5	60.3
Sometimes	115	19.7	22.2	73.2	82.5
Rarely_Never	52	8.9	10.0	82.1	92.5
Dont_know	38	6.5	7.3	88.5	99.8
Scantron_Error	1	0.2	0.2	88.7	100.0
NA	66	11.3	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	809	46.1	48.1	46.1	48.1
Sometimes	504	28.7	30.0	74.9	78.1
Rarely_Never	243	13.9	14.4	88.7	92.5
Dont_know	126	7.2	7.5	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	72	4.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	700	39.9	43.3	39.9	43.3
Sometimes	580	33.1	35.9	73.0	79.2
Rarely_Never	218	12.4	13.5	85.4	92.7
Dont_know	116	6.6	7.2	92.0	99.9
Scantron_Error	2	0.1	0.1	92.1	100.0
NA	138	7.9	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	950	54.2	59.3	54.2	59.3
Sometimes	362	20.6	22.6	74.8	81.9
Rarely_Never	146	8.3	9.1	83.1	91.0
Dont_know	144	8.2	9.0	91.3	100.0
Scantron_Error	0	0.0	0.0	91.3	100.0
NA	152	8.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	5	31.2	33.3	31.2	33.3
Sometimes	8	50.0	53.3	81.2	86.7
Rarely_Never	1	6.2	6.7	87.5	93.3
Dont_know	1	6.2	6.7	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	3	18.8	20.0	18.8	20.0
Sometimes	7	43.8	46.7	62.5	66.7
Rarely_Never	2	12.5	13.3	75.0	80.0
Dont_know	3	18.8	20.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	7	43.8	46.7	43.8	46.7
Sometimes	5	31.2	33.3	75.0	80.0
Rarely_Never	0	0.0	0.0	75.0	80.0
Dont_know	3	18.8	20.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C4C

C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
1. How many neighbors do/did you know by name?
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Often
  - 2=Sometimes
  - 3=Rarely/Never
  - 88=Don’t Know

All data

  c4c1 <- as.factor(d[,"c4c1"])
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	998	28.1	29.5	28.1	29.5
Sometimes	1378	38.7	40.7	66.8	70.2
Rarely_Never	944	26.5	27.9	93.3	98.1
Dont_know	62	1.7	1.8	95.1	99.9
Scantron_Error	2	0.1	0.1	95.1	100.0
NA	173	4.9	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	1053	29.6	32.6	29.6	32.6
Sometimes	1383	38.9	42.8	68.5	75.4
Rarely_Never	699	19.7	21.6	88.1	97.0
Dont_know	94	2.6	2.9	90.8	99.9
Scantron_Error	2	0.1	0.1	90.8	100.0
NA	326	9.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	1933	54.3	60.3	54.3	60.3
Sometimes	766	21.5	23.9	75.9	84.1
Rarely_Never	361	10.1	11.3	86.0	95.4
Dont_know	146	4.1	4.6	90.1	99.9
Scantron_Error	2	0.1	0.1	90.2	100.0
NA	349	9.8	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	74	23.1	24.0	23.1	24.0
Sometimes	133	41.4	43.2	64.5	67.2
Rarely_Never	97	30.2	31.5	94.7	98.7
Dont_know	4	1.2	1.3	96.0	100.0
Scantron_Error	0	0.0	0.0	96.0	100.0
NA	13	4.0	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	85	26.5	28.1	26.5	28.1
Sometimes	141	43.9	46.5	70.4	74.6
Rarely_Never	75	23.4	24.8	93.8	99.3
Dont_know	2	0.6	0.7	94.4	100.0
Scantron_Error	0	0.0	0.0	94.4	100.0
NA	18	5.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	194	60.4	64.7	60.4	64.7
Sometimes	62	19.3	20.7	79.8	85.3
Rarely_Never	35	10.9	11.7	90.7	97.0
Dont_know	9	2.8	3.0	93.5	100.0
Scantron_Error	0	0.0	0.0	93.5	100.0
NA	21	6.5	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	60	28.6	29.6	28.6	29.6
Sometimes	86	41.0	42.4	69.5	71.9
Rarely_Never	51	24.3	25.1	93.8	97.0
Dont_know	6	2.9	3.0	96.7	100.0
Scantron_Error	0	0.0	0.0	96.7	100.0
NA	7	3.3	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	58	27.6	29.3	27.6	29.3
Sometimes	95	45.2	48.0	72.9	77.3
Rarely_Never	41	19.5	20.7	92.4	98.0
Dont_know	4	1.9	2.0	94.3	100.0
Scantron_Error	0	0.0	0.0	94.3	100.0
NA	12	5.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	121	57.6	61.7	57.6	61.7
Sometimes	47	22.4	24.0	80.0	85.7
Rarely_Never	17	8.1	8.7	88.1	94.4
Dont_know	11	5.2	5.6	93.3	100.0
Scantron_Error	0	0.0	0.0	93.3	100.0
NA	14	6.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	80	25.4	27.1	25.4	27.1
Sometimes	114	36.2	38.6	61.6	65.8
Rarely_Never	100	31.7	33.9	93.3	99.7
Dont_know	1	0.3	0.3	93.7	100.0
Scantron_Error	0	0.0	0.0	93.7	100.0
NA	20	6.3	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	72	22.9	25.4	22.9	25.4
Sometimes	116	36.8	41.0	59.7	66.4
Rarely_Never	82	26.0	29.0	85.7	95.4
Dont_know	13	4.1	4.6	89.8	100.0
Scantron_Error	0	0.0	0.0	89.8	100.0
NA	32	10.2	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	160	50.8	56.3	50.8	56.3
Sometimes	70	22.2	24.6	73.0	81.0
Rarely_Never	37	11.7	13.0	84.8	94.0
Dont_know	17	5.4	6.0	90.2	100.0
Scantron_Error	0	0.0	0.0	90.2	100.0
NA	31	9.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	94	26.4	28.1	26.4	28.1
Sometimes	143	40.2	42.7	66.6	70.7
Rarely_Never	93	26.1	27.8	92.7	98.5
Dont_know	5	1.4	1.5	94.1	100.0
Scantron_Error	0	0.0	0.0	94.1	100.0
NA	21	5.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	114	32.0	35.5	32.0	35.5
Sometimes	132	37.1	41.1	69.1	76.6
Rarely_Never	66	18.5	20.6	87.6	97.2
Dont_know	9	2.5	2.8	90.2	100.0
Scantron_Error	0	0.0	0.0	90.2	100.0
NA	35	9.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	179	50.3	57.6	50.3	57.6
Sometimes	76	21.3	24.4	71.6	82.0
Rarely_Never	40	11.2	12.9	82.9	94.9
Dont_know	15	4.2	4.8	87.1	99.7
Scantron_Error	1	0.3	0.3	87.4	100.0
NA	45	12.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	222	37.9	39.7	37.9	39.7
Sometimes	209	35.7	37.4	73.7	77.1
Rarely_Never	112	19.1	20.0	92.8	97.1
Dont_know	15	2.6	2.7	95.4	99.8
Scantron_Error	1	0.2	0.2	95.6	100.0
NA	26	4.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	213	36.4	41.1	36.4	41.1
Sometimes	222	37.9	42.9	74.4	84.0
Rarely_Never	68	11.6	13.1	86.0	97.1
Dont_know	14	2.4	2.7	88.4	99.8
Scantron_Error	1	0.2	0.2	88.5	100.0
NA	67	11.5	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	314	53.7	60.5	53.7	60.5
Sometimes	126	21.5	24.3	75.2	84.8
Rarely_Never	57	9.7	11.0	85.0	95.8
Dont_know	21	3.6	4.0	88.5	99.8
Scantron_Error	1	0.2	0.2	88.7	100.0
NA	66	11.3	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	463	26.4	27.7	26.4	27.7
Sometimes	688	39.2	41.2	65.6	69.0
Rarely_Never	488	27.8	29.2	93.4	98.2
Dont_know	29	1.7	1.7	95.1	99.9
Scantron_Error	1	0.1	0.1	95.2	100.0
NA	85	4.8	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	508	29.0	31.8	29.0	31.8
Sometimes	671	38.3	42.1	67.2	73.9
Rarely_Never	364	20.8	22.8	88.0	96.7
Dont_know	51	2.9	3.2	90.9	99.9
Scantron_Error	1	0.1	0.1	90.9	100.0
NA	159	9.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	959	54.7	60.5	54.7	60.5
Sometimes	379	21.6	23.9	76.3	84.4
Rarely_Never	175	10.0	11.0	86.3	95.5
Dont_know	72	4.1	4.5	90.4	100.0
Scantron_Error	0	0.0	0.0	90.4	100.0
NA	169	9.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	5	31.2	33.3	31.2	33.3
Sometimes	5	31.2	33.3	62.5	66.7
Rarely_Never	3	18.8	20.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	3	18.8	23.1	18.8	23.1
Sometimes	6	37.5	46.2	56.2	69.2
Rarely_Never	3	18.8	23.1	75.0	92.3
Dont_know	1	6.2	7.7	81.2	100.0
Scantron_Error	0	0.0	0.0	81.2	100.0
NA	3	18.8	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	6	37.5	46.2	37.5	46.2
Sometimes	6	37.5	46.2	75.0	92.3
Rarely_Never	0	0.0	0.0	75.0	92.3
Dont_know	1	6.2	7.7	81.2	100.0
Scantron_Error	0	0.0	0.0	81.2	100.0
NA	3	18.8	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C4D

C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
1. How many neighbors do/did you have a friendly talk with at least once a week?
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Often
  - 2=Sometimes
  - 3=Rarely/Never
  - 88=Don’t Know

All data

  c4d1 <- as.factor(d[,"c4d1"])
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	463	13.0	13.6	13.0	13.6
Sometimes	1249	35.1	36.8	48.1	50.5
Rarely_Never	1617	45.5	47.7	93.6	98.1
Dont_know	61	1.7	1.8	95.3	99.9
Scantron_Error	3	0.1	0.1	95.4	100.0
NA	164	4.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	588	16.5	18.1	16.5	18.1
Sometimes	1418	39.9	43.6	56.4	61.7
Rarely_Never	1132	31.8	34.8	88.2	96.5
Dont_know	112	3.1	3.4	91.4	100.0
Scantron_Error	1	0.0	0.0	91.4	100.0
NA	306	8.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	1418	39.9	43.9	39.9	43.9
Sometimes	987	27.7	30.5	67.6	74.4
Rarely_Never	631	17.7	19.5	85.4	94.0
Dont_know	194	5.5	6.0	90.8	100.0
Scantron_Error	1	0.0	0.0	90.8	100.0
NA	326	9.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	37	11.5	11.9	11.5	11.9
Sometimes	117	36.4	37.5	48.0	49.4
Rarely_Never	153	47.7	49.0	95.6	98.4
Dont_know	5	1.6	1.6	97.2	100.0
Scantron_Error	0	0.0	0.0	97.2	100.0
NA	9	2.8	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	53	16.5	17.2	16.5	17.2
Sometimes	133	41.4	43.2	57.9	60.4
Rarely_Never	116	36.1	37.7	94.1	98.1
Dont_know	6	1.9	1.9	96.0	100.0
Scantron_Error	0	0.0	0.0	96.0	100.0
NA	13	4.0	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	163	50.8	53.3	50.8	53.3
Sometimes	83	25.9	27.1	76.6	80.4
Rarely_Never	43	13.4	14.1	90.0	94.4
Dont_know	17	5.3	5.6	95.3	100.0
Scantron_Error	0	0.0	0.0	95.3	100.0
NA	15	4.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	35	16.7	17.5	16.7	17.5
Sometimes	67	31.9	33.5	48.6	51.0
Rarely_Never	95	45.2	47.5	93.8	98.5
Dont_know	3	1.4	1.5	95.2	100.0
Scantron_Error	0	0.0	0.0	95.2	100.0
NA	10	4.8	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	43	20.5	21.9	20.5	21.9
Sometimes	79	37.6	40.3	58.1	62.2
Rarely_Never	71	33.8	36.2	91.9	98.5
Dont_know	3	1.4	1.5	93.3	100.0
Scantron_Error	0	0.0	0.0	93.3	100.0
NA	14	6.7	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	87	41.4	44.8	41.4	44.8
Sometimes	62	29.5	32.0	71.0	76.8
Rarely_Never	35	16.7	18.0	87.6	94.8
Dont_know	10	4.8	5.2	92.4	100.0
Scantron_Error	0	0.0	0.0	92.4	100.0
NA	16	7.6	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	47	14.9	15.5	14.9	15.5
Sometimes	110	34.9	36.3	49.8	51.8
Rarely_Never	143	45.4	47.2	95.2	99.0
Dont_know	3	1.0	1.0	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	38	12.1	13.1	12.1	13.1
Sometimes	119	37.8	40.9	49.8	54.0
Rarely_Never	119	37.8	40.9	87.6	94.8
Dont_know	15	4.8	5.2	92.4	100.0
Scantron_Error	0	0.0	0.0	92.4	100.0
NA	24	7.6	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	116	36.8	39.6	36.8	39.6
Sometimes	99	31.4	33.8	68.3	73.4
Rarely_Never	58	18.4	19.8	86.7	93.2
Dont_know	20	6.3	6.8	93.0	100.0
Scantron_Error	0	0.0	0.0	93.0	100.0
NA	22	7.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	48	13.5	14.2	13.5	14.2
Sometimes	140	39.3	41.4	52.8	55.6
Rarely_Never	140	39.3	41.4	92.1	97.0
Dont_know	10	2.8	3.0	94.9	100.0
Scantron_Error	0	0.0	0.0	94.9	100.0
NA	18	5.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	62	17.4	19.3	17.4	19.3
Sometimes	162	45.5	50.5	62.9	69.8
Rarely_Never	83	23.3	25.9	86.2	95.6
Dont_know	14	3.9	4.4	90.2	100.0
Scantron_Error	0	0.0	0.0	90.2	100.0
NA	35	9.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	135	37.9	42.9	37.9	42.9
Sometimes	93	26.1	29.5	64.0	72.4
Rarely_Never	63	17.7	20.0	81.7	92.4
Dont_know	24	6.7	7.6	88.5	100.0
Scantron_Error	0	0.0	0.0	88.5	100.0
NA	41	11.5	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	106	18.1	19.2	18.1	19.2
Sometimes	217	37.1	39.3	55.2	58.5
Rarely_Never	216	36.9	39.1	92.1	97.6
Dont_know	11	1.9	2.0	94.0	99.6
Scantron_Error	2	0.3	0.4	94.4	100.0
NA	33	5.6	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	121	20.7	23.4	20.7	23.4
Sometimes	230	39.3	44.4	60.0	67.8
Rarely_Never	149	25.5	28.8	85.5	96.5
Dont_know	17	2.9	3.3	88.4	99.8
Scantron_Error	1	0.2	0.2	88.5	100.0
NA	67	11.5	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	230	39.3	44.7	39.3	44.7
Sometimes	163	27.9	31.7	67.2	76.5
Rarely_Never	97	16.6	18.9	83.8	95.3
Dont_know	23	3.9	4.5	87.7	99.8
Scantron_Error	1	0.2	0.2	87.9	100.0
NA	71	12.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	183	10.4	10.9	10.4	10.9
Sometimes	593	33.8	35.5	44.2	46.4
Rarely_Never	866	49.4	51.8	93.6	98.2
Dont_know	29	1.7	1.7	95.3	99.9
Scantron_Error	1	0.1	0.1	95.3	100.0
NA	82	4.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	266	15.2	16.6	15.2	16.6
Sometimes	689	39.3	43.0	54.4	59.6
Rarely_Never	590	33.6	36.8	88.1	96.4
Dont_know	57	3.2	3.6	91.3	100.0
Scantron_Error	0	0.0	0.0	91.3	100.0
NA	152	8.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	678	38.7	42.5	38.7	42.5
Sometimes	482	27.5	30.2	66.1	72.8
Rarely_Never	334	19.0	21.0	85.2	93.7
Dont_know	100	5.7	6.3	90.9	100.0
Scantron_Error	0	0.0	0.0	90.9	100.0
NA	160	9.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	7	43.8	43.8	43.8	43.8
Sometimes	5	31.2	31.2	75.0	75.0
Rarely_Never	4	25.0	25.0	100.0	100.0
Dont_know	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	5	31.2	33.3	31.2	33.3
Sometimes	6	37.5	40.0	68.8	73.3
Rarely_Never	4	25.0	26.7	93.8	100.0
Dont_know	0	0.0	0.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	9	56.2	60.0	56.2	60.0
Sometimes	5	31.2	33.3	87.5	93.3
Rarely_Never	1	6.2	6.7	93.8	100.0
Dont_know	0	0.0	0.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

C4E

C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
- 1. Current (from prostate cancer diagnosis to present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Often
  - 2=Sometimes
  - 3=Rarely/Never
  - 88=Don’t Know

All data

  c4e1 <- as.factor(d[,"c4e1"])
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88",
                     Scantron_Error="*")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	562	15.8	17.1	15.8	17.1
Sometimes	1104	31.0	33.5	46.8	50.6
Rarely_Never	1327	37.3	40.3	84.1	90.9
Dont_know	296	8.3	9.0	92.5	99.9
Scantron_Error	4	0.1	0.1	92.6	100.0
NA	264	7.4	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	613	17.2	19.4	17.2	19.4
Sometimes	1186	33.3	37.6	50.6	57.0
Rarely_Never	1087	30.6	34.4	81.1	91.4
Dont_know	270	7.6	8.5	88.7	99.9
Scantron_Error	2	0.1	0.1	88.8	100.0
NA	399	11.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	1253	35.2	39.8	35.2	39.8
Sometimes	947	26.6	30.1	61.8	69.8
Rarely_Never	641	18.0	20.3	79.9	90.2
Dont_know	307	8.6	9.7	88.5	99.9
Scantron_Error	2	0.1	0.1	88.6	100.0
NA	407	11.4	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	39	12.1	12.8	12.1	12.8
Sometimes	105	32.7	34.4	44.9	47.2
Rarely_Never	141	43.9	46.2	88.8	93.4
Dont_know	20	6.2	6.6	95.0	100.0
Scantron_Error	0	0.0	0.0	95.0	100.0
NA	16	5.0	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	43	13.4	14.2	13.4	14.2
Sometimes	135	42.1	44.7	55.5	58.9
Rarely_Never	107	33.3	35.4	88.8	94.4
Dont_know	16	5.0	5.3	93.8	99.7
Scantron_Error	1	0.3	0.3	94.1	100.0
NA	19	5.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	139	43.3	46.2	43.3	46.2
Sometimes	88	27.4	29.2	70.7	75.4
Rarely_Never	51	15.9	16.9	86.6	92.4
Dont_know	23	7.2	7.6	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	20	6.2	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	38	18.1	19.3	18.1	19.3
Sometimes	61	29.0	31.0	47.1	50.3
Rarely_Never	86	41.0	43.7	88.1	93.9
Dont_know	11	5.2	5.6	93.3	99.5
Scantron_Error	1	0.5	0.5	93.8	100.0
NA	13	6.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	43	20.5	22.3	20.5	22.3
Sometimes	78	37.1	40.4	57.6	62.7
Rarely_Never	60	28.6	31.1	86.2	93.8
Dont_know	12	5.7	6.2	91.9	100.0
Scantron_Error	0	0.0	0.0	91.9	100.0
NA	17	8.1	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	75	35.7	39.1	35.7	39.1
Sometimes	59	28.1	30.7	63.8	69.8
Rarely_Never	41	19.5	21.4	83.3	91.1
Dont_know	17	8.1	8.9	91.4	100.0
Scantron_Error	0	0.0	0.0	91.4	100.0
NA	18	8.6	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	51	16.2	17.7	16.2	17.7
Sometimes	109	34.6	37.8	50.8	55.6
Rarely_Never	106	33.7	36.8	84.4	92.4
Dont_know	22	7.0	7.6	91.4	100.0
Scantron_Error	0	0.0	0.0	91.4	100.0
NA	27	8.6	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	43	13.7	15.5	13.7	15.5
Sometimes	102	32.4	36.8	46.0	52.3
Rarely_Never	105	33.3	37.9	79.4	90.3
Dont_know	27	8.6	9.7	87.9	100.0
Scantron_Error	0	0.0	0.0	87.9	100.0
NA	38	12.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	99	31.4	35.4	31.4	35.4
Sometimes	91	28.9	32.5	60.3	67.9
Rarely_Never	63	20.0	22.5	80.3	90.4
Dont_know	27	8.6	9.6	88.9	100.0
Scantron_Error	0	0.0	0.0	88.9	100.0
NA	35	11.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	51	14.3	15.4	14.3	15.4
Sometimes	116	32.6	34.9	46.9	50.3
Rarely_Never	134	37.6	40.4	84.6	90.7
Dont_know	31	8.7	9.3	93.3	100.0
Scantron_Error	0	0.0	0.0	93.3	100.0
NA	24	6.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	55	15.4	17.3	15.4	17.3
Sometimes	123	34.6	38.7	50.0	56.0
Rarely_Never	110	30.9	34.6	80.9	90.6
Dont_know	30	8.4	9.4	89.3	100.0
Scantron_Error	0	0.0	0.0	89.3	100.0
NA	38	10.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	117	32.9	37.4	32.9	37.4
Sometimes	101	28.4	32.3	61.2	69.6
Rarely_Never	63	17.7	20.1	78.9	89.8
Dont_know	32	9.0	10.2	87.9	100.0
Scantron_Error	0	0.0	0.0	87.9	100.0
NA	43	12.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	118	20.2	22.0	20.2	22.0
Sometimes	180	30.8	33.5	50.9	55.5
Rarely_Never	187	32.0	34.8	82.9	90.3
Dont_know	51	8.7	9.5	91.6	99.8
Scantron_Error	1	0.2	0.2	91.8	100.0
NA	48	8.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	120	20.5	23.9	20.5	23.9
Sometimes	185	31.6	36.9	52.1	60.8
Rarely_Never	156	26.7	31.1	78.8	91.8
Dont_know	40	6.8	8.0	85.6	99.8
Scantron_Error	1	0.2	0.2	85.8	100.0
NA	83	14.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	207	35.4	41.2	35.4	41.2
Sometimes	149	25.5	29.7	60.9	70.9
Rarely_Never	94	16.1	18.7	76.9	89.6
Dont_know	50	8.5	10.0	85.5	99.6
Scantron_Error	2	0.3	0.4	85.8	100.0
NA	83	14.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	264	15.1	16.3	15.1	16.3
Sometimes	527	30.0	32.6	45.1	48.9
Rarely_Never	667	38.0	41.2	83.1	90.1
Dont_know	159	9.1	9.8	92.2	99.9
Scantron_Error	2	0.1	0.1	92.3	100.0
NA	135	7.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	308	17.6	19.9	17.6	19.9
Sometimes	557	31.8	35.9	49.3	55.8
Rarely_Never	543	31.0	35.0	80.3	90.8
Dont_know	143	8.2	9.2	88.4	100.0
Scantron_Error	0	0.0	0.0	88.4	100.0
NA	203	11.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	612	34.9	39.6	34.9	39.6
Sometimes	453	25.8	29.3	60.7	68.8
Rarely_Never	326	18.6	21.1	79.3	89.9
Dont_know	156	8.9	10.1	88.2	100.0
Scantron_Error	0	0.0	0.0	88.2	100.0
NA	207	11.8	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

1. Current (from prostate cancer diagnosis to present)
	n	%	val%	%cum	val%cum
Often	1	6.2	6.7	6.2	6.7
Sometimes	6	37.5	40.0	43.8	46.7
Rarely_Never	6	37.5	40.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%	%cum	val%cum
Often	1	6.2	6.7	6.2	6.7
Sometimes	6	37.5	40.0	43.8	46.7
Rarely_Never	6	37.5	40.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

3. Childhood or young adult life (up to age 30)
	n	%	val%	%cum	val%cum
Often	4	25.0	26.7	25.0	26.7
Sometimes	6	37.5	40.0	62.5	66.7
Rarely_Never	3	18.8	20.0	81.2	86.7
Dont_know	2	12.5	13.3	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

D1

D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
- 1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
- 1. For unfair reasons, have you ever not been hired for a job?
- 1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
- 1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
- 1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
- 1. Have you ever been unfairly denied a bank loan?
- 1. Have you ever been unfairly treated when getting medical care?
  - 1=No
  - 2=Yes
- If yes, How stressful was this experience?
  - 1=Not at all
  - 2=A little
  - 3=Somewhat
  - 4=Extremely ## All data

# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
  levels(d1aa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1aa <- ordered(d1aa, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1ab <- ordered(d1ab, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	1779	50.0	52.0
Yes	1639	46.1	47.9
Scantron_Error	1	0.0	0.0
NA	138	3.9	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	11	0.3	100	0.3	100
NA	3546	99.7	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  levels(d1ba) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ba <- ordered(d1ba, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1bb <- ordered(d1bb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	2072	58.3	61.6
Yes	1289	36.2	38.3
Scantron_Error	1	0.0	0.0
NA	195	5.5	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	9	0.3	100	0.3	100
NA	3548	99.7	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  levels(d1ca) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ca <- ordered(d1ca, c( "No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	1682	47.3	49.4
Yes	1721	48.4	50.6
Scantron_Error	0	0.0	0.0
NA	154	4.3	NA
Total	3557	100.0	100.0

  d1cb <- as.factor(d[,"d1cb"])
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1cb <- ordered(d1cb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	11	0.3	100	0.3	100
NA	3546	99.7	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  levels(d1da) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1da <- ordered(d1da, c( "No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	2714	76.3	79.8
Yes	689	19.4	20.2
Scantron_Error	0	0.0	0.0
NA	154	4.3	NA
Total	3557	100.0	100.0

  d1db <- as.factor(d[,"d1db"])
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1db <- ordered(d1db, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	7	0.2	100	0.2	100
NA	3550	99.8	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  levels(d1ea) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ea <- ordered(d1ea, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	2863	80.5	83.6
Yes	561	15.8	16.4
Scantron_Error	0	0.0	0.0
NA	133	3.7	NA
Total	3557	100.0	100.0

  d1eb <- as.factor(d[,"d1eb"])
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1eb <- ordered(d1eb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	6	0.2	100	0.2	100
NA	3551	99.8	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  levels(d1fa) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1fa <- ordered(d1fa, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	2439	68.6	71.9
Yes	953	26.8	28.1
Scantron_Error	0	0.0	0.0
NA	165	4.6	NA
Total	3557	100.0	100.0

  d1fb <- as.factor(d[,"d1fb"])
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1fb <- ordered(d1fb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	7	0.2	100	0.2	100
NA	3550	99.8	NA	100.0	NA
Total	3557	100.0	100	100.0	100

# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  levels(d1ga) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")
  d1ga <- ordered(d1ga, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	2856	80.3	84
Yes	543	15.3	16
Scantron_Error	0	0.0	0
NA	158	4.4	NA
Total	3557	100.0	100

  d1gb <- as.factor(d[,"d1gb"])
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4",
                     Scantron_Error="*")
  d1gb <- ordered(d1gb, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	4	0.1	100	0.1	100
NA	3553	99.9	NA	100.0	NA
Total	3557	100.0	100	100.0	100

LA County

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	158	49.2	50.3
Yes	156	48.6	49.7
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	177	55.1	56.7
Yes	135	42.1	43.3
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	70	21.8	22.2
Yes	245	76.3	77.8
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	222	69.2	71.8
Yes	87	27.1	28.2
Scantron_Error	0	0.0	0.0
NA	12	3.7	NA
Total	321	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	233	72.6	74.2
Yes	81	25.2	25.8
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	228	71.0	73.3
Yes	83	25.9	26.7
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	256	79.8	81.8
Yes	57	17.8	18.2
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	321	100	NA	100	NA
Total	321	100	100	100	100

Northern CA

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	100	47.6	49.3
Yes	103	49.0	50.7
Scantron_Error	0	0.0	0.0
NA	7	3.3	NA
Total	210	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	120	57.1	58.8
Yes	84	40.0	41.2
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	0.5	100	0.5	100
NA	209	99.5	NA	100.0	NA
Total	210	100.0	100	100.0	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	79	37.6	38.5
Yes	126	60.0	61.5
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	0	0	0
Yes	0	0	0	0	0
Scantron_Error	2	1	100	1	100
NA	208	99	NA	100	NA
Total	210	100	100	100	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	146	69.5	72.3
Yes	56	26.7	27.7
Scantron_Error	0	0.0	0.0
NA	8	3.8	NA
Total	210	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	148	70.5	74.4
Yes	51	24.3	25.6
Scantron_Error	0	0.0	0.0
NA	11	5.2	NA
Total	210	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	150	71.4	75.4
Yes	49	23.3	24.6
Scantron_Error	0	0.0	0.0
NA	11	5.2	NA
Total	210	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	152	72.4	75.6
Yes	49	23.3	24.4
Scantron_Error	0	0.0	0.0
NA	9	4.3	NA
Total	210	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	210	100	NA	100	NA
Total	210	100	100	100	100

Greater CA

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	134	42.5	44.4
Yes	168	53.3	55.6
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	145	46.0	49
Yes	151	47.9	51
Scantron_Error	0	0.0	0
NA	19	6.0	NA
Total	315	100.0	100

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	113	35.9	37.5
Yes	188	59.7	62.5
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	224	71.1	74.4
Yes	77	24.4	25.6
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	2	0.6	100	0.6	100
NA	313	99.4	NA	100.0	NA
Total	315	100.0	100	100.0	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	219	69.5	72.5
Yes	83	26.3	27.5
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	0.3	100	0.3	100
NA	314	99.7	NA	100.0	NA
Total	315	100.0	100	100.0	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	214	67.9	71.3
Yes	86	27.3	28.7
Scantron_Error	0	0.0	0.0
NA	15	4.8	NA
Total	315	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	247	78.4	81.2
Yes	57	18.1	18.8
Scantron_Error	0	0.0	0.0
NA	11	3.5	NA
Total	315	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	315	100	NA	100	NA
Total	315	100	100	100	100

Detroit

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	192	53.9	56.3
Yes	148	41.6	43.4
Scantron_Error	1	0.3	0.3
NA	15	4.2	NA
Total	356	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	0.3	100	0.3	100
NA	355	99.7	NA	100.0	NA
Total	356	100.0	100	100.0	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	212	59.6	64.4
Yes	116	32.6	35.3
Scantron_Error	1	0.3	0.3
NA	27	7.6	NA
Total	356	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	138	38.8	41.2
Yes	197	55.3	58.8
Scantron_Error	0	0.0	0.0
NA	21	5.9	NA
Total	356	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	249	69.9	73.9
Yes	88	24.7	26.1
Scantron_Error	0	0.0	0.0
NA	19	5.3	NA
Total	356	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	275	77.2	80.9
Yes	65	18.3	19.1
Scantron_Error	0	0.0	0.0
NA	16	4.5	NA
Total	356	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	248	69.7	74.3
Yes	86	24.2	25.7
Scantron_Error	0	0.0	0.0
NA	22	6.2	NA
Total	356	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	282	79.2	84.4
Yes	52	14.6	15.6
Scantron_Error	0	0.0	0.0
NA	22	6.2	NA
Total	356	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	356	100	NA	100	NA
Total	356	100	100	100	100

Louisiana

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	317	54.2	57.1
Yes	238	40.7	42.9
Scantron_Error	0	0.0	0.0
NA	30	5.1	NA
Total	585	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	2	0.3	100	0.3	100
NA	583	99.7	NA	100.0	NA
Total	585	100.0	100	100.0	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	364	62.2	66.3
Yes	185	31.6	33.7
Scantron_Error	0	0.0	0.0
NA	36	6.2	NA
Total	585	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	0.2	100	0.2	100
NA	584	99.8	NA	100.0	NA
Total	585	100.0	100	100.0	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	298	50.9	53.6
Yes	258	44.1	46.4
Scantron_Error	0	0.0	0.0
NA	29	5.0	NA
Total	585	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	3	0.5	100	0.5	100
NA	582	99.5	NA	100.0	NA
Total	585	100.0	100	100.0	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	466	79.7	83.2
Yes	94	16.1	16.8
Scantron_Error	0	0.0	0.0
NA	25	4.3	NA
Total	585	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	503	86.0	89.5
Yes	59	10.1	10.5
Scantron_Error	0	0.0	0.0
NA	23	3.9	NA
Total	585	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	383	65.5	68.8
Yes	174	29.7	31.2
Scantron_Error	0	0.0	0.0
NA	28	4.8	NA
Total	585	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	0.2	100	0.2	100
NA	584	99.8	NA	100.0	NA
Total	585	100.0	100	100.0	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	468	80.0	84
Yes	89	15.2	16
Scantron_Error	0	0.0	0
NA	28	4.8	NA
Total	585	100.0	100

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0	NaN	0	NaN
Yes	0	0	NaN	0	NaN
Scantron_Error	0	0	NaN	0	NaN
NA	585	100	NA	100	NA
Total	585	100	100	100	100

Georgia

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	870	49.6	51.5
Yes	819	46.7	48.5
Scantron_Error	0	0.0	0.0
NA	65	3.7	NA
Total	1754	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	7	0.4	100	0.4	100
NA	1747	99.6	NA	100.0	NA
Total	1754	100.0	100	100.0	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	1047	59.7	63.2
Yes	609	34.7	36.8
Scantron_Error	0	0.0	0.0
NA	98	5.6	NA
Total	1754	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	6	0.3	100	0.3	100
NA	1748	99.7	NA	100.0	NA
Total	1754	100.0	100	100.0	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	976	55.6	58.3
Yes	699	39.9	41.7
Scantron_Error	0	0.0	0.0
NA	79	4.5	NA
Total	1754	100.0	100.0

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	5	0.3	100	0.3	100
NA	1749	99.7	NA	100.0	NA
Total	1754	100.0	100	100.0	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	1399	79.8	83.4
Yes	279	15.9	16.6
Scantron_Error	0	0.0	0.0
NA	76	4.3	NA
Total	1754	100.0	100.0

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	4	0.2	100	0.2	100
NA	1750	99.8	NA	100.0	NA
Total	1754	100.0	100	100.0	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	1471	83.9	87
Yes	220	12.5	13
Scantron_Error	0	0.0	0
NA	63	3.6	NA
Total	1754	100.0	100

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	4	0.2	100	0.2	100
NA	1750	99.8	NA	100.0	NA
Total	1754	100.0	100	100.0	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	1203	68.6	71.8
Yes	472	26.9	28.2
Scantron_Error	0	0.0	0.0
NA	79	4.5	NA
Total	1754	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	5	0.3	100	0.3	100
NA	1749	99.7	NA	100.0	NA
Total	1754	100.0	100	100.0	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	1437	81.9	85.8
Yes	237	13.5	14.2
Scantron_Error	0	0.0	0.0
NA	80	4.6	NA
Total	1754	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	3	0.2	100	0.2	100
NA	1751	99.8	NA	100.0	NA
Total	1754	100.0	100	100.0	100

Michigan

a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
	n	%	val%
No	8	50.0	53.3
Yes	7	43.8	46.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

a. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

b. For unfair reasons, have you ever not been hired for a job?
	n	%	val%
No	7	43.8	43.8
Yes	9	56.2	56.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

b. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
	n	%	val%
No	8	50	50
Yes	8	50	50
Scantron_Error	0	0	0
Total	16	100	100

c. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
	n	%	val%
No	8	50	50
Yes	8	50	50
Scantron_Error	0	0	0
Total	16	100	100

d. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
	n	%	val%
No	14	87.5	87.5
Yes	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

e. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

f. Have you ever been unfairly denied a bank loan?
	n	%	val%
No	13	81.2	81.2
Yes	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

f. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

g. Have you ever been unfairly treated when getting medical care?
	n	%	val%
No	14	87.5	87.5
Yes	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

g. If yes, How stressful was this experience?
	n	%	val%	%cum	val%cum
No	0	0.0	0	0.0	0
Yes	0	0.0	0	0.0	0
Scantron_Error	1	6.2	100	6.2	100
NA	15	93.8	NA	100.0	NA
Total	16	100.0	100	100.0	100

D2

D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.

All data

# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	495	13.9	14.6
Somewhat_Agree	1436	40.4	42.4
Somewhat_Disagree	820	23.1	24.2
Strongly_Disagree	629	17.7	18.6
Scantron_Error	8	0.2	0.2
NA	169	4.8	NA
Total	3557	100.0	100.0

# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	429	12.1	12.6
Somewhat_Agree	1222	34.4	36.0
Somewhat_Disagree	1002	28.2	29.5
Strongly_Disagree	740	20.8	21.8
Scantron_Error	6	0.2	0.2
NA	158	4.4	NA
Total	3557	100.0	100.0

# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	650	18.3	19.7
Somewhat_Agree	1104	31.0	33.5
Somewhat_Disagree	824	23.2	25.0
Strongly_Disagree	707	19.9	21.5
Scantron_Error	8	0.2	0.2
NA	264	7.4	NA
Total	3557	100.0	100.0

# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	1841	51.8	54.7
Somewhat_Agree	900	25.3	26.7
Somewhat_Disagree	332	9.3	9.9
Strongly_Disagree	289	8.1	8.6
Scantron_Error	3	0.1	0.1
NA	192	5.4	NA
Total	3557	100.0	100.0

# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	149	4.2	4.5
Somewhat_Agree	498	14.0	15.1
Somewhat_Disagree	1447	40.7	44.0
Strongly_Disagree	1195	33.6	36.3
Scantron_Error	0	0.0	0.0
NA	268	7.5	NA
Total	3557	100.0	100.0

LA County

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	49	15.3	15.7
Somewhat_Agree	139	43.3	44.6
Somewhat_Disagree	79	24.6	25.3
Strongly_Disagree	45	14.0	14.4
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	34	10.6	10.8
Somewhat_Agree	120	37.4	38.2
Somewhat_Disagree	100	31.2	31.8
Strongly_Disagree	60	18.7	19.1
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	81	25.2	26.2
Somewhat_Agree	101	31.5	32.7
Somewhat_Disagree	81	25.2	26.2
Strongly_Disagree	46	14.3	14.9
Scantron_Error	0	0.0	0.0
NA	12	3.7	NA
Total	321	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	179	55.8	57.0
Somewhat_Agree	95	29.6	30.3
Somewhat_Disagree	27	8.4	8.6
Strongly_Disagree	13	4.0	4.1
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	13	4.0	4.2
Somewhat_Agree	45	14.0	14.6
Somewhat_Disagree	154	48.0	50.0
Strongly_Disagree	96	29.9	31.2
Scantron_Error	0	0.0	0.0
NA	13	4.0	NA
Total	321	100.0	100.0

Northern CA

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	32	15.2	16
Somewhat_Agree	80	38.1	40
Somewhat_Disagree	46	21.9	23
Strongly_Disagree	42	20.0	21
Scantron_Error	0	0.0	0
NA	10	4.8	NA
Total	210	100.0	100

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	18	8.6	8.9
Somewhat_Agree	63	30.0	31.2
Somewhat_Disagree	61	29.0	30.2
Strongly_Disagree	60	28.6	29.7
Scantron_Error	0	0.0	0.0
NA	8	3.8	NA
Total	210	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	46	21.9	23.4
Somewhat_Agree	58	27.6	29.4
Somewhat_Disagree	38	18.1	19.3
Strongly_Disagree	55	26.2	27.9
Scantron_Error	0	0.0	0.0
NA	13	6.2	NA
Total	210	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	112	53.3	56.6
Somewhat_Agree	60	28.6	30.3
Somewhat_Disagree	13	6.2	6.6
Strongly_Disagree	13	6.2	6.6
Scantron_Error	0	0.0	0.0
NA	12	5.7	NA
Total	210	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	15	7.1	7.7
Somewhat_Agree	25	11.9	12.8
Somewhat_Disagree	84	40.0	43.1
Strongly_Disagree	71	33.8	36.4
Scantron_Error	0	0.0	0.0
NA	15	7.1	NA
Total	210	100.0	100.0

Greater CA

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	51	16.2	17.1
Somewhat_Agree	116	36.8	38.8
Somewhat_Disagree	71	22.5	23.7
Strongly_Disagree	61	19.4	20.4
Scantron_Error	0	0.0	0.0
NA	16	5.1	NA
Total	315	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	27	8.6	9.0
Somewhat_Agree	85	27.0	28.3
Somewhat_Disagree	112	35.6	37.3
Strongly_Disagree	76	24.1	25.3
Scantron_Error	0	0.0	0.0
NA	15	4.8	NA
Total	315	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	50	15.9	17.5
Somewhat_Agree	92	29.2	32.2
Somewhat_Disagree	71	22.5	24.8
Strongly_Disagree	73	23.2	25.5
Scantron_Error	0	0.0	0.0
NA	29	9.2	NA
Total	315	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	165	52.4	55.9
Somewhat_Agree	78	24.8	26.4
Somewhat_Disagree	30	9.5	10.2
Strongly_Disagree	21	6.7	7.1
Scantron_Error	1	0.3	0.3
NA	20	6.3	NA
Total	315	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	18	5.7	6.2
Somewhat_Agree	51	16.2	17.6
Somewhat_Disagree	130	41.3	44.8
Strongly_Disagree	91	28.9	31.4
Scantron_Error	0	0.0	0.0
NA	25	7.9	NA
Total	315	100.0	100.0

Detroit

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	46	12.9	13.8
Somewhat_Agree	131	36.8	39.2
Somewhat_Disagree	86	24.2	25.7
Strongly_Disagree	71	19.9	21.3
Scantron_Error	0	0.0	0.0
NA	22	6.2	NA
Total	356	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	38	10.7	11.3
Somewhat_Agree	125	35.1	37.2
Somewhat_Disagree	88	24.7	26.2
Strongly_Disagree	84	23.6	25.0
Scantron_Error	1	0.3	0.3
NA	20	5.6	NA
Total	356	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	56	15.7	17.4
Somewhat_Agree	107	30.1	33.3
Somewhat_Disagree	80	22.5	24.9
Strongly_Disagree	77	21.6	24.0
Scantron_Error	1	0.3	0.3
NA	35	9.8	NA
Total	356	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	171	48.0	51.7
Somewhat_Agree	90	25.3	27.2
Somewhat_Disagree	35	9.8	10.6
Strongly_Disagree	35	9.8	10.6
Scantron_Error	0	0.0	0.0
NA	25	7.0	NA
Total	356	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	18	5.1	5.6
Somewhat_Agree	47	13.2	14.7
Somewhat_Disagree	129	36.2	40.3
Strongly_Disagree	126	35.4	39.4
Scantron_Error	0	0.0	0.0
NA	36	10.1	NA
Total	356	100.0	100.0

Louisiana

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	82	14.0	14.8
Somewhat_Agree	223	38.1	40.3
Somewhat_Disagree	137	23.4	24.8
Strongly_Disagree	107	18.3	19.3
Scantron_Error	4	0.7	0.7
NA	32	5.5	NA
Total	585	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	78	13.3	14.2
Somewhat_Agree	199	34.0	36.1
Somewhat_Disagree	144	24.6	26.1
Strongly_Disagree	129	22.1	23.4
Scantron_Error	1	0.2	0.2
NA	34	5.8	NA
Total	585	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	102	17.4	19.3
Somewhat_Agree	165	28.2	31.2
Somewhat_Disagree	133	22.7	25.1
Strongly_Disagree	129	22.1	24.4
Scantron_Error	0	0.0	0.0
NA	56	9.6	NA
Total	585	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	275	47.0	49.5
Somewhat_Agree	152	26.0	27.4
Somewhat_Disagree	62	10.6	11.2
Strongly_Disagree	65	11.1	11.7
Scantron_Error	1	0.2	0.2
NA	30	5.1	NA
Total	585	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	22	3.8	4.1
Somewhat_Agree	77	13.2	14.3
Somewhat_Disagree	236	40.3	43.7
Strongly_Disagree	205	35.0	38.0
Scantron_Error	0	0.0	0.0
NA	45	7.7	NA
Total	585	100.0	100.0

Georgia

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	234	13.3	14.0
Somewhat_Agree	741	42.2	44.3
Somewhat_Disagree	398	22.7	23.8
Strongly_Disagree	297	16.9	17.7
Scantron_Error	4	0.2	0.2
NA	80	4.6	NA
Total	1754	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	233	13.3	13.9
Somewhat_Agree	625	35.6	37.2
Somewhat_Disagree	492	28.1	29.3
Strongly_Disagree	326	18.6	19.4
Scantron_Error	4	0.2	0.2
NA	74	4.2	NA
Total	1754	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	313	17.8	19.1
Somewhat_Agree	578	33.0	35.4
Somewhat_Disagree	417	23.8	25.5
Strongly_Disagree	321	18.3	19.6
Scantron_Error	6	0.3	0.4
NA	119	6.8	NA
Total	1754	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	933	53.2	56.3
Somewhat_Agree	419	23.9	25.3
Somewhat_Disagree	163	9.3	9.8
Strongly_Disagree	140	8.0	8.5
Scantron_Error	1	0.1	0.1
NA	98	5.6	NA
Total	1754	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	62	3.5	3.8
Somewhat_Agree	251	14.3	15.5
Somewhat_Disagree	708	40.4	43.7
Strongly_Disagree	599	34.2	37.0
Scantron_Error	0	0.0	0.0
NA	134	7.6	NA
Total	1754	100.0	100.0

Michigan

a. Patients have sometimes been deceived or misled at hospitals.
	n	%	val%
Strongly_Agree	1	6.2	6.2
Somewhat_Agree	6	37.5	37.5
Somewhat_Disagree	3	18.8	18.8
Strongly_Disagree	6	37.5	37.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

b. Hospitals often want to know more about your personal affairs or business than they really need to know.
	n	%	val%
Strongly_Agree	1	6.2	6.2
Somewhat_Agree	5	31.2	31.2
Somewhat_Disagree	5	31.2	31.2
Strongly_Disagree	5	31.2	31.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
	n	%	val%
Strongly_Agree	2	12.5	12.5
Somewhat_Agree	3	18.8	18.8
Somewhat_Disagree	4	25.0	25.0
Strongly_Disagree	6	37.5	37.5
Scantron_Error	1	6.2	6.2
Total	16	100.0	100.0

d. Rich patients receive better care at hospitals than poor patients.
	n	%	val%
Strongly_Agree	6	37.5	37.5
Somewhat_Agree	6	37.5	37.5
Somewhat_Disagree	2	12.5	12.5
Strongly_Disagree	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

e. Male patients receive better care at hospitals than female patients.
	n	%	val%
Strongly_Agree	1	6.2	6.2
Somewhat_Agree	2	12.5	12.5
Somewhat_Disagree	6	37.5	37.5
Strongly_Disagree	7	43.8	43.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

D3A

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. You have been treated with less respect than other people
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3a1 <- as.factor(d[,"d3a1"])
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1048	29.5	30.2
Rarely	1014	28.5	29.2
Sometimes	1207	33.9	34.8
Often	198	5.6	5.7
Scantron_Error	2	0.1	0.1
NA	88	2.5	NA
Total	3557	100.0	100.0

#2
  d3a2 <- as.factor(d[,"d3a2"])
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	782	22.0	23.8
Rarely	957	26.9	29.1
Sometimes	1275	35.8	38.8
Often	269	7.6	8.2
Scantron_Error	2	0.1	0.1
NA	272	7.6	NA
Total	3557	100.0	100.0

#3
  d3a3 <- as.factor(d[,"d3a3"])
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	719	20.2	22.1
Rarely	717	20.2	22.0
Sometimes	1238	34.8	38.0
Often	576	16.2	17.7
Scantron_Error	4	0.1	0.1
NA	303	8.5	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	84	26.2	26.4
Rarely	100	31.2	31.4
Sometimes	116	36.1	36.5
Often	18	5.6	5.7
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	57	17.8	18.2
Rarely	89	27.7	28.4
Sometimes	146	45.5	46.6
Often	21	6.5	6.7
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	54	16.8	17.3
Rarely	72	22.4	23.0
Sometimes	142	44.2	45.4
Often	45	14.0	14.4
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	52	24.8	25.5
Rarely	56	26.7	27.5
Sometimes	75	35.7	36.8
Often	21	10.0	10.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	40	19.0	20.4
Rarely	59	28.1	30.1
Sometimes	74	35.2	37.8
Often	23	11.0	11.7
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	41	19.5	21.1
Rarely	45	21.4	23.2
Sometimes	66	31.4	34.0
Often	42	20.0	21.6
Scantron_Error	0	0.0	0.0
NA	16	7.6	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	65	20.6	21.5
Rarely	89	28.3	29.5
Sometimes	122	38.7	40.4
Often	26	8.3	8.6
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	45	14.3	15.5
Rarely	79	25.1	27.2
Sometimes	129	41.0	44.5
Often	37	11.7	12.8
Scantron_Error	0	0.0	0.0
NA	25	7.9	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	48	15.2	16.5
Rarely	59	18.7	20.3
Sometimes	116	36.8	39.9
Often	67	21.3	23.0
Scantron_Error	1	0.3	0.3
NA	24	7.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	111	31.2	31.6
Rarely	122	34.3	34.8
Sometimes	103	28.9	29.3
Often	15	4.2	4.3
Scantron_Error	0	0.0	0.0
NA	5	1.4	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	81	22.8	25.2
Rarely	117	32.9	36.3
Sometimes	104	29.2	32.3
Often	20	5.6	6.2
Scantron_Error	0	0.0	0.0
NA	34	9.6	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	78	21.9	24.5
Rarely	87	24.4	27.3
Sometimes	117	32.9	36.7
Often	37	10.4	11.6
Scantron_Error	0	0.0	0.0
NA	37	10.4	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	185	31.6	32.3
Rarely	141	24.1	24.7
Sometimes	206	35.2	36.0
Often	39	6.7	6.8
Scantron_Error	1	0.2	0.2
NA	13	2.2	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	144	24.6	27.5
Rarely	132	22.6	25.2
Sometimes	195	33.3	37.2
Often	52	8.9	9.9
Scantron_Error	1	0.2	0.2
NA	61	10.4	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	131	22.4	25.1
Rarely	95	16.2	18.2
Sometimes	202	34.5	38.7
Often	93	15.9	17.8
Scantron_Error	1	0.2	0.2
NA	63	10.8	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	545	31.1	31.9
Rarely	501	28.6	29.4
Sometimes	580	33.1	34.0
Often	79	4.5	4.6
Scantron_Error	1	0.1	0.1
NA	48	2.7	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	410	23.4	25.2
Rarely	478	27.3	29.4
Sometimes	620	35.3	38.2
Often	116	6.6	7.1
Scantron_Error	1	0.1	0.1
NA	129	7.4	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	366	20.9	22.9
Rarely	351	20.0	21.9
Sometimes	591	33.7	36.9
Often	291	16.6	18.2
Scantron_Error	1	0.1	0.1
NA	154	8.8	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	6	37.5	37.5
Rarely	5	31.2	31.2
Sometimes	5	31.2	31.2
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	5	31.2	33.3
Rarely	3	18.8	20.0
Sometimes	7	43.8	46.7
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	1	6.2	6.7
Rarely	8	50.0	53.3
Sometimes	4	25.0	26.7
Often	1	6.2	6.7
Scantron_Error	1	6.2	6.7
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3B

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. You have received poorer service than other people at restaurants or stores
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3b1 <- as.factor(d[,"d3b1"])
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	795	22.4	23.1
Rarely	1124	31.6	32.7
Sometimes	1344	37.8	39.0
Often	178	5.0	5.2
Scantron_Error	1	0.0	0.0
NA	115	3.2	NA
Total	3557	100.0	100.0

#2
  d3b2 <- as.factor(d[,"d3b2"])
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	601	16.9	18.4
Rarely	978	27.5	30.0
Sometimes	1451	40.8	44.5
Often	229	6.4	7.0
Scantron_Error	0	0.0	0.0
NA	298	8.4	NA
Total	3557	100.0	100.0

#3
  d3b3 <- as.factor(d[,"d3b3"])
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	603	17.0	18.7
Rarely	728	20.5	22.6
Sometimes	1332	37.4	41.4
Often	556	15.6	17.3
Scantron_Error	0	0.0	0.0
NA	338	9.5	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	64	19.9	20.2
Rarely	116	36.1	36.6
Sometimes	119	37.1	37.5
Often	18	5.6	5.7
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	46	14.3	14.8
Rarely	102	31.8	32.8
Sometimes	144	44.9	46.3
Often	19	5.9	6.1
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	40	12.5	12.9
Rarely	95	29.6	30.6
Sometimes	142	44.2	45.8
Often	33	10.3	10.6
Scantron_Error	0	0.0	0.0
NA	11	3.4	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	35	16.7	17.2
Rarely	66	31.4	32.4
Sometimes	84	40.0	41.2
Often	19	9.0	9.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	29	13.8	14.9
Rarely	58	27.6	29.9
Sometimes	84	40.0	43.3
Often	23	11.0	11.9
Scantron_Error	0	0.0	0.0
NA	16	7.6	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	32	15.2	16.8
Rarely	43	20.5	22.5
Sometimes	85	40.5	44.5
Often	31	14.8	16.2
Scantron_Error	0	0.0	0.0
NA	19	9.0	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	66	21.0	22.0
Rarely	94	29.8	31.3
Sometimes	121	38.4	40.3
Often	18	5.7	6.0
Scantron_Error	1	0.3	0.3
NA	15	4.8	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	48	15.2	16.5
Rarely	85	27.0	29.2
Sometimes	135	42.9	46.4
Often	23	7.3	7.9
Scantron_Error	0	0.0	0.0
NA	24	7.6	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	51	16.2	17.7
Rarely	56	17.8	19.4
Sometimes	122	38.7	42.4
Often	59	18.7	20.5
Scantron_Error	0	0.0	0.0
NA	27	8.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	88	24.7	25.1
Rarely	123	34.6	35.1
Sometimes	119	33.4	34.0
Often	20	5.6	5.7
Scantron_Error	0	0.0	0.0
NA	6	1.7	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	63	17.7	19.6
Rarely	112	31.5	34.8
Sometimes	126	35.4	39.1
Often	21	5.9	6.5
Scantron_Error	0	0.0	0.0
NA	34	9.6	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	63	17.7	19.9
Rarely	85	23.9	26.9
Sometimes	133	37.4	42.1
Often	35	9.8	11.1
Scantron_Error	0	0.0	0.0
NA	40	11.2	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	160	27.4	28.6
Rarely	153	26.2	27.4
Sometimes	216	36.9	38.6
Often	30	5.1	5.4
Scantron_Error	0	0.0	0.0
NA	26	4.4	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	111	19.0	21.7
Rarely	131	22.4	25.6
Sometimes	225	38.5	44.0
Often	44	7.5	8.6
Scantron_Error	0	0.0	0.0
NA	74	12.6	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	105	17.9	20.5
Rarely	91	15.6	17.8
Sometimes	212	36.2	41.4
Often	104	17.8	20.3
Scantron_Error	0	0.0	0.0
NA	73	12.5	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	376	21.4	22.2
Rarely	568	32.4	33.5
Sometimes	681	38.8	40.2
Often	71	4.0	4.2
Scantron_Error	0	0.0	0.0
NA	58	3.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	298	17.0	18.5
Rarely	486	27.7	30.1
Sometimes	734	41.8	45.4
Often	97	5.5	6.0
Scantron_Error	0	0.0	0.0
NA	139	7.9	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	308	17.6	19.4
Rarely	354	20.2	22.3
Sometimes	634	36.1	39.9
Often	291	16.6	18.3
Scantron_Error	0	0.0	0.0
NA	167	9.5	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	6	37.5	37.5
Rarely	4	25.0	25.0
Sometimes	4	25.0	25.0
Often	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	6	37.5	40.0
Rarely	4	25.0	26.7
Sometimes	3	18.8	20.0
Often	2	12.5	13.3
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	4	25.0	26.7
Rarely	4	25.0	26.7
Sometimes	4	25.0	26.7
Often	3	18.8	20.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3C

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. People have acted as if they think you are not smart
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3c1 <- as.factor(d[,"d3c1"])
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	968	27.2	28.3
Rarely	1055	29.7	30.9
Sometimes	1118	31.4	32.7
Often	276	7.8	8.1
Scantron_Error	1	0.0	0.0
NA	139	3.9	NA
Total	3557	100.0	100.0

#2
  d3c2 <- as.factor(d[,"d3c2"])
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	788	22.2	24.3
Rarely	1016	28.6	31.3
Sometimes	1154	32.4	35.6
Often	286	8.0	8.8
Scantron_Error	0	0.0	0.0
NA	313	8.8	NA
Total	3557	100.0	100.0

#3
  d3c3 <- as.factor(d[,"d3c3"])
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	725	20.4	22.5
Rarely	812	22.8	25.2
Sometimes	1194	33.6	37.1
Often	486	13.7	15.1
Scantron_Error	0	0.0	0.0
NA	340	9.6	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	72	22.4	22.9
Rarely	113	35.2	36.0
Sometimes	103	32.1	32.8
Often	26	8.1	8.3
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	57	17.8	18.6
Rarely	97	30.2	31.6
Sometimes	128	39.9	41.7
Often	25	7.8	8.1
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	52	16.2	17.0
Rarely	82	25.5	26.8
Sometimes	135	42.1	44.1
Often	37	11.5	12.1
Scantron_Error	0	0.0	0.0
NA	15	4.7	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	42	20.0	20.4
Rarely	72	34.3	35.0
Sometimes	70	33.3	34.0
Often	22	10.5	10.7
Scantron_Error	0	0.0	0.0
NA	4	1.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	41	19.5	20.8
Rarely	64	30.5	32.5
Sometimes	69	32.9	35.0
Often	23	11.0	11.7
Scantron_Error	0	0.0	0.0
NA	13	6.2	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	39	18.6	20.0
Rarely	52	24.8	26.7
Sometimes	69	32.9	35.4
Often	35	16.7	17.9
Scantron_Error	0	0.0	0.0
NA	15	7.1	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	79	25.1	26.6
Rarely	88	27.9	29.6
Sometimes	105	33.3	35.4
Often	25	7.9	8.4
Scantron_Error	0	0.0	0.0
NA	18	5.7	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	61	19.4	21.3
Rarely	76	24.1	26.5
Sometimes	120	38.1	41.8
Often	30	9.5	10.5
Scantron_Error	0	0.0	0.0
NA	28	8.9	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	60	19.0	21.1
Rarely	63	20.0	22.1
Sometimes	106	33.7	37.2
Often	56	17.8	19.6
Scantron_Error	0	0.0	0.0
NA	30	9.5	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	106	29.8	31.0
Rarely	106	29.8	31.0
Sometimes	108	30.3	31.6
Often	22	6.2	6.4
Scantron_Error	0	0.0	0.0
NA	14	3.9	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	85	23.9	26.6
Rarely	103	28.9	32.2
Sometimes	104	29.2	32.5
Often	28	7.9	8.8
Scantron_Error	0	0.0	0.0
NA	36	10.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	80	22.5	25.5
Rarely	87	24.4	27.7
Sometimes	107	30.1	34.1
Often	40	11.2	12.7
Scantron_Error	0	0.0	0.0
NA	42	11.8	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	165	28.2	29.6
Rarely	141	24.1	25.3
Sometimes	200	34.2	35.9
Often	51	8.7	9.2
Scantron_Error	0	0.0	0.0
NA	28	4.8	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	138	23.6	26.6
Rarely	141	24.1	27.2
Sometimes	188	32.1	36.3
Often	51	8.7	9.8
Scantron_Error	0	0.0	0.0
NA	67	11.5	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	126	21.5	24.4
Rarely	122	20.9	23.6
Sometimes	180	30.8	34.8
Often	89	15.2	17.2
Scantron_Error	0	0.0	0.0
NA	68	11.6	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	499	28.4	29.6
Rarely	531	30.3	31.5
Sometimes	527	30.0	31.2
Often	129	7.4	7.6
Scantron_Error	1	0.1	0.1
NA	67	3.8	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	402	22.9	25.1
Rarely	530	30.2	33.1
Sometimes	543	31.0	33.9
Often	127	7.2	7.9
Scantron_Error	0	0.0	0.0
NA	152	8.7	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	363	20.7	22.9
Rarely	403	23.0	25.4
Sometimes	594	33.9	37.4
Often	227	12.9	14.3
Scantron_Error	0	0.0	0.0
NA	167	9.5	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	5	31.2	33.3
Rarely	4	25.0	26.7
Sometimes	5	31.2	33.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	4	25.0	30.8
Rarely	5	31.2	38.5
Sometimes	2	12.5	15.4
Often	2	12.5	15.4
Scantron_Error	0	0.0	0.0
NA	3	18.8	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	5	31.2	38.5
Rarely	3	18.8	23.1
Sometimes	3	18.8	23.1
Often	2	12.5	15.4
Scantron_Error	0	0.0	0.0
NA	3	18.8	NA
Total	16	100.0	100.0

rm(temp.dd)

D3D

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. People have acted as if they are afraid of you
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3d1 <- as.factor(d[,"d3d1"])
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1289	36.2	37.4
Rarely	961	27.0	27.9
Sometimes	974	27.4	28.3
Often	218	6.1	6.3
Scantron_Error	0	0.0	0.0
NA	115	3.2	NA
Total	3557	100.0	100.0

#2
  d3d2 <- as.factor(d[,"d3d2"])
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	1025	28.8	31.5
Rarely	874	24.6	26.8
Sometimes	1064	29.9	32.7
Often	294	8.3	9.0
Scantron_Error	0	0.0	0.0
NA	300	8.4	NA
Total	3557	100.0	100.0

#3
  d3d3 <- as.factor(d[,"d3d3"])
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	1096	30.8	33.9
Rarely	795	22.4	24.6
Sometimes	961	27.0	29.7
Often	380	10.7	11.8
Scantron_Error	1	0.0	0.0
NA	324	9.1	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	90	28.0	28.8
Rarely	88	27.4	28.1
Sometimes	106	33.0	33.9
Often	29	9.0	9.3
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	71	22.1	23.0
Rarely	74	23.1	23.9
Sometimes	130	40.5	42.1
Often	34	10.6	11.0
Scantron_Error	0	0.0	0.0
NA	12	3.7	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	75	23.4	24.4
Rarely	73	22.7	23.7
Sometimes	119	37.1	38.6
Often	41	12.8	13.3
Scantron_Error	0	0.0	0.0
NA	13	4.0	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	41	19.5	20.1
Rarely	63	30.0	30.9
Sometimes	77	36.7	37.7
Often	23	11.0	11.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	34	16.2	17.3
Rarely	54	25.7	27.6
Sometimes	76	36.2	38.8
Often	32	15.2	16.3
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	42	20.0	21.6
Rarely	51	24.3	26.3
Sometimes	69	32.9	35.6
Often	32	15.2	16.5
Scantron_Error	0	0.0	0.0
NA	16	7.6	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	81	25.7	26.8
Rarely	93	29.5	30.8
Sometimes	101	32.1	33.4
Often	27	8.6	8.9
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	67	21.3	23.1
Rarely	75	23.8	25.9
Sometimes	106	33.7	36.6
Often	42	13.3	14.5
Scantron_Error	0	0.0	0.0
NA	25	7.9	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	74	23.5	25.7
Rarely	65	20.6	22.6
Sometimes	101	32.1	35.1
Often	48	15.2	16.7
Scantron_Error	0	0.0	0.0
NA	27	8.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	127	35.7	37.0
Rarely	101	28.4	29.4
Sometimes	93	26.1	27.1
Often	22	6.2	6.4
Scantron_Error	0	0.0	0.0
NA	13	3.7	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	100	28.1	30.9
Rarely	88	24.7	27.2
Sometimes	107	30.1	33.0
Often	29	8.1	9.0
Scantron_Error	0	0.0	0.0
NA	32	9.0	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	111	31.2	34.9
Rarely	84	23.6	26.4
Sometimes	86	24.2	27.0
Often	37	10.4	11.6
Scantron_Error	0	0.0	0.0
NA	38	10.7	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	247	42.2	44.1
Rarely	135	23.1	24.1
Sometimes	155	26.5	27.7
Often	23	3.9	4.1
Scantron_Error	0	0.0	0.0
NA	25	4.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	185	31.6	36.1
Rarely	137	23.4	26.7
Sometimes	152	26.0	29.6
Often	39	6.7	7.6
Scantron_Error	0	0.0	0.0
NA	72	12.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	197	33.7	38.2
Rarely	118	20.2	22.9
Sometimes	147	25.1	28.5
Often	54	9.2	10.5
Scantron_Error	0	0.0	0.0
NA	69	11.8	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	699	39.9	41.0
Rarely	478	27.3	28.1
Sometimes	436	24.9	25.6
Often	91	5.2	5.3
Scantron_Error	0	0.0	0.0
NA	50	2.9	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	564	32.2	35.0
Rarely	442	25.2	27.5
Sometimes	489	27.9	30.4
Often	115	6.6	7.1
Scantron_Error	0	0.0	0.0
NA	144	8.2	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	593	33.8	37.2
Rarely	400	22.8	25.1
Sometimes	436	24.9	27.4
Often	164	9.4	10.3
Scantron_Error	1	0.1	0.1
NA	160	9.1	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	4	25.0	25.0
Rarely	3	18.8	18.8
Sometimes	6	37.5	37.5
Often	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	4	25.0	26.7
Rarely	4	25.0	26.7
Sometimes	4	25.0	26.7
Often	3	18.8	20.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	4	25.0	26.7
Rarely	4	25.0	26.7
Sometimes	3	18.8	20.0
Often	4	25.0	26.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3E

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. People have acted as if they think you are dishonest
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3e1 <- as.factor(d[,"d3e1"])
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1498	42.1	43.5
Rarely	1001	28.1	29.1
Sometimes	765	21.5	22.2
Often	174	4.9	5.1
Scantron_Error	3	0.1	0.1
NA	116	3.3	NA
Total	3557	100.0	100.0

#2
  d3e2 <- as.factor(d[,"d3e2"])
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	1207	33.9	37.1
Rarely	951	26.7	29.2
Sometimes	885	24.9	27.2
Often	211	5.9	6.5
Scantron_Error	1	0.0	0.0
NA	302	8.5	NA
Total	3557	100.0	100.0

#3
  d3e3 <- as.factor(d[,"d3e3"])
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	1179	33.1	36.5
Rarely	793	22.3	24.5
Sometimes	904	25.4	28.0
Often	356	10.0	11.0
Scantron_Error	1	0.0	0.0
NA	324	9.1	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	116	36.1	36.7
Rarely	103	32.1	32.6
Sometimes	78	24.3	24.7
Often	19	5.9	6.0
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	89	27.7	28.5
Rarely	106	33.0	34.0
Sometimes	95	29.6	30.4
Often	22	6.9	7.1
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	86	26.8	27.6
Rarely	85	26.5	27.2
Sometimes	111	34.6	35.6
Often	30	9.3	9.6
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	67	31.9	32.7
Rarely	62	29.5	30.2
Sometimes	58	27.6	28.3
Often	18	8.6	8.8
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	57	27.1	29.1
Rarely	54	25.7	27.6
Sometimes	65	31.0	33.2
Often	20	9.5	10.2
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	53	25.2	27.2
Rarely	51	24.3	26.2
Sometimes	65	31.0	33.3
Often	26	12.4	13.3
Scantron_Error	0	0.0	0.0
NA	15	7.1	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	118	37.5	39.2
Rarely	94	29.8	31.2
Sometimes	74	23.5	24.6
Often	15	4.8	5.0
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	88	27.9	30.4
Rarely	87	27.6	30.1
Sometimes	89	28.3	30.8
Often	25	7.9	8.7
Scantron_Error	0	0.0	0.0
NA	26	8.3	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	83	26.3	28.9
Rarely	75	23.8	26.1
Sometimes	93	29.5	32.4
Often	36	11.4	12.5
Scantron_Error	0	0.0	0.0
NA	28	8.9	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	144	40.4	42.0
Rarely	103	28.9	30.0
Sometimes	77	21.6	22.4
Often	19	5.3	5.5
Scantron_Error	0	0.0	0.0
NA	13	3.7	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	115	32.3	36.1
Rarely	93	26.1	29.2
Sometimes	89	25.0	27.9
Often	22	6.2	6.9
Scantron_Error	0	0.0	0.0
NA	37	10.4	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	113	31.7	35.9
Rarely	78	21.9	24.8
Sometimes	93	26.1	29.5
Often	31	8.7	9.8
Scantron_Error	0	0.0	0.0
NA	41	11.5	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	262	44.8	46.8
Rarely	136	23.2	24.3
Sometimes	132	22.6	23.6
Often	29	5.0	5.2
Scantron_Error	1	0.2	0.2
NA	25	4.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	208	35.6	40.4
Rarely	131	22.4	25.4
Sometimes	145	24.8	28.2
Often	31	5.3	6.0
Scantron_Error	0	0.0	0.0
NA	70	12.0	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	209	35.7	40.4
Rarely	106	18.1	20.5
Sometimes	139	23.8	26.9
Often	62	10.6	12.0
Scantron_Error	1	0.2	0.2
NA	68	11.6	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	787	44.9	46.3
Rarely	497	28.3	29.2
Sometimes	341	19.4	20.0
Often	74	4.2	4.4
Scantron_Error	2	0.1	0.1
NA	53	3.0	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	646	36.8	40.1
Rarely	475	27.1	29.5
Sometimes	397	22.6	24.7
Often	90	5.1	5.6
Scantron_Error	1	0.1	0.1
NA	145	8.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	630	35.9	39.6
Rarely	394	22.5	24.7
Sometimes	398	22.7	25.0
Often	170	9.7	10.7
Scantron_Error	0	0.0	0.0
NA	162	9.2	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	4	25.0	26.7
Rarely	6	37.5	40.0
Sometimes	5	31.2	33.3
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	4	25.0	26.7
Rarely	5	31.2	33.3
Sometimes	5	31.2	33.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	5	31.2	33.3
Rarely	4	25.0	26.7
Sometimes	5	31.2	33.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3F

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. People have acted as if they’re better than you are
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3f1 <- as.factor(d[,"d3f1"])
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	550	15.5	16.0
Rarely	948	26.7	27.6
Sometimes	1500	42.2	43.6
Often	441	12.4	12.8
Scantron_Error	1	0.0	0.0
NA	117	3.3	NA
Total	3557	100.0	100.0

#2
  d3f2 <- as.factor(d[,"d3f2"])
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	408	11.5	12.5
Rarely	836	23.5	25.6
Sometimes	1553	43.7	47.6
Often	468	13.2	14.3
Scantron_Error	1	0.0	0.0
NA	291	8.2	NA
Total	3557	100.0	100.0

#3
  d3f3 <- as.factor(d[,"d3f3"])
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	413	11.6	12.7
Rarely	664	18.7	20.5
Sometimes	1415	39.8	43.6
Often	747	21.0	23.0
Scantron_Error	4	0.1	0.1
NA	314	8.8	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	44	13.7	14.0
Rarely	95	29.6	30.2
Sometimes	133	41.4	42.2
Often	43	13.4	13.7
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	38	11.8	12.2
Rarely	82	25.5	26.3
Sometimes	143	44.5	45.8
Often	49	15.3	15.7
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	42	13.1	13.5
Rarely	69	21.5	22.3
Sometimes	142	44.2	45.8
Often	57	17.8	18.4
Scantron_Error	0	0.0	0.0
NA	11	3.4	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	24	11.4	11.7
Rarely	52	24.8	25.4
Sometimes	97	46.2	47.3
Often	32	15.2	15.6
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	23	11.0	11.7
Rarely	49	23.3	25.0
Sometimes	91	43.3	46.4
Often	33	15.7	16.8
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	23	11.0	11.8
Rarely	33	15.7	16.9
Sometimes	95	45.2	48.7
Often	44	21.0	22.6
Scantron_Error	0	0.0	0.0
NA	15	7.1	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	44	14.0	14.7
Rarely	79	25.1	26.4
Sometimes	128	40.6	42.8
Often	48	15.2	16.1
Scantron_Error	0	0.0	0.0
NA	16	5.1	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	32	10.2	11.1
Rarely	63	20.0	21.8
Sometimes	139	44.1	48.1
Often	55	17.5	19.0
Scantron_Error	0	0.0	0.0
NA	26	8.3	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	30	9.5	10.5
Rarely	64	20.3	22.5
Sometimes	111	35.2	38.9
Often	80	25.4	28.1
Scantron_Error	0	0.0	0.0
NA	30	9.5	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	58	16.3	17.0
Rarely	91	25.6	26.6
Sometimes	150	42.1	43.9
Often	43	12.1	12.6
Scantron_Error	0	0.0	0.0
NA	14	3.9	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	36	10.1	11.4
Rarely	88	24.7	27.8
Sometimes	146	41.0	46.2
Often	46	12.9	14.6
Scantron_Error	0	0.0	0.0
NA	40	11.2	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	42	11.8	13.4
Rarely	80	22.5	25.6
Sometimes	125	35.1	39.9
Often	66	18.5	21.1
Scantron_Error	0	0.0	0.0
NA	43	12.1	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	91	15.6	16.1
Rarely	126	21.5	22.3
Sometimes	258	44.1	45.6
Often	90	15.4	15.9
Scantron_Error	1	0.2	0.2
NA	19	3.2	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	62	10.6	11.9
Rarely	114	19.5	21.8
Sometimes	263	45.0	50.4
Often	82	14.0	15.7
Scantron_Error	1	0.2	0.2
NA	63	10.8	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	68	11.6	12.9
Rarely	93	15.9	17.7
Sometimes	230	39.3	43.7
Often	133	22.7	25.3
Scantron_Error	2	0.3	0.4
NA	59	10.1	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	284	16.2	16.7
Rarely	503	28.7	29.6
Sometimes	726	41.4	42.8
Often	184	10.5	10.8
Scantron_Error	0	0.0	0.0
NA	57	3.2	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	214	12.2	13.2
Rarely	437	24.9	27.0
Sometimes	763	43.5	47.2
Often	202	11.5	12.5
Scantron_Error	0	0.0	0.0
NA	138	7.9	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	204	11.6	12.8
Rarely	322	18.4	20.1
Sometimes	708	40.4	44.3
Often	363	20.7	22.7
Scantron_Error	2	0.1	0.1
NA	155	8.8	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	5	31.2	31.2
Rarely	2	12.5	12.5
Sometimes	8	50.0	50.0
Often	1	6.2	6.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	3	18.8	20.0
Rarely	3	18.8	20.0
Sometimes	8	50.0	53.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	4	25.0	26.7
Rarely	3	18.8	20.0
Sometimes	4	25.0	26.7
Often	4	25.0	26.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3G

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. You have been called names or insulted
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3g1 <- as.factor(d[,"d3g1"])
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1247	35.1	36.3
Rarely	1180	33.2	34.4
Sometimes	869	24.4	25.3
Often	136	3.8	4.0
Scantron_Error	2	0.1	0.1
NA	123	3.5	NA
Total	3557	100.0	100.0

#2
  d3g2 <- as.factor(d[,"d3g2"])
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	815	22.9	25.0
Rarely	1177	33.1	36.1
Sometimes	1099	30.9	33.7
Often	171	4.8	5.2
Scantron_Error	0	0.0	0.0
NA	295	8.3	NA
Total	3557	100.0	100.0

#3
  d3g3 <- as.factor(d[,"d3g3"])
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	566	15.9	17.5
Rarely	836	23.5	25.8
Sometimes	1339	37.6	41.3
Often	497	14.0	15.3
Scantron_Error	3	0.1	0.1
NA	316	8.9	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	125	38.9	39.7
Rarely	121	37.7	38.4
Sometimes	62	19.3	19.7
Often	7	2.2	2.2
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	62	19.3	19.9
Rarely	137	42.7	44.1
Sometimes	103	32.1	33.1
Often	9	2.8	2.9
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	32	10.0	10.3
Rarely	104	32.4	33.3
Sometimes	144	44.9	46.2
Often	32	10.0	10.3
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	50	23.8	24.4
Rarely	83	39.5	40.5
Sometimes	64	30.5	31.2
Often	8	3.8	3.9
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	32	15.2	16.3
Rarely	82	39.0	41.8
Sometimes	68	32.4	34.7
Often	14	6.7	7.1
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	30	14.3	15.3
Rarely	46	21.9	23.5
Sometimes	87	41.4	44.4
Often	33	15.7	16.8
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	96	30.5	31.8
Rarely	124	39.4	41.1
Sometimes	73	23.2	24.2
Often	9	2.9	3.0
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	62	19.7	21.2
Rarely	108	34.3	36.9
Sometimes	106	33.7	36.2
Often	17	5.4	5.8
Scantron_Error	0	0.0	0.0
NA	22	7.0	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	44	14.0	15.1
Rarely	61	19.4	21.0
Sometimes	130	41.3	44.7
Often	56	17.8	19.2
Scantron_Error	0	0.0	0.0
NA	24	7.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	114	32.0	33.1
Rarely	111	31.2	32.3
Sometimes	102	28.7	29.7
Often	17	4.8	4.9
Scantron_Error	0	0.0	0.0
NA	12	3.4	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	80	22.5	24.8
Rarely	112	31.5	34.7
Sometimes	114	32.0	35.3
Often	17	4.8	5.3
Scantron_Error	0	0.0	0.0
NA	33	9.3	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	64	18.0	20.2
Rarely	78	21.9	24.6
Sometimes	136	38.2	42.9
Often	39	11.0	12.3
Scantron_Error	0	0.0	0.0
NA	39	11.0	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	189	32.3	34.0
Rarely	167	28.5	30.0
Sometimes	166	28.4	29.9
Often	34	5.8	6.1
Scantron_Error	0	0.0	0.0
NA	29	5.0	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	122	20.9	23.7
Rarely	158	27.0	30.7
Sometimes	195	33.3	37.9
Often	39	6.7	7.6
Scantron_Error	0	0.0	0.0
NA	71	12.1	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	92	15.7	17.9
Rarely	106	18.1	20.6
Sometimes	221	37.8	42.9
Often	96	16.4	18.6
Scantron_Error	0	0.0	0.0
NA	70	12.0	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	669	38.1	39.4
Rarely	568	32.4	33.5
Sometimes	396	22.6	23.3
Often	61	3.5	3.6
Scantron_Error	2	0.1	0.1
NA	58	3.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	453	25.8	28.1
Rarely	578	33.0	35.9
Sometimes	505	28.8	31.4
Often	74	4.2	4.6
Scantron_Error	0	0.0	0.0
NA	144	8.2	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	301	17.2	18.9
Rarely	441	25.1	27.6
Sometimes	612	34.9	38.4
Often	239	13.6	15.0
Scantron_Error	2	0.1	0.1
NA	159	9.1	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	4	25.0	25.0
Rarely	6	37.5	37.5
Sometimes	6	37.5	37.5
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	4	25.0	26.7
Rarely	2	12.5	13.3
Sometimes	8	50.0	53.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	3	18.8	20.0
Rarely	0	0.0	0.0
Sometimes	9	56.2	60.0
Often	2	12.5	13.3
Scantron_Error	1	6.2	6.7
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3H

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. You have been threatened or harassed
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3h1 <- as.factor(d[,"d3h1"])
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1905	53.6	55.6
Rarely	1002	28.2	29.2
Sometimes	466	13.1	13.6
Often	52	1.5	1.5
Scantron_Error	2	0.1	0.1
NA	130	3.7	NA
Total	3557	100.0	100.0

#2
  d3h2 <- as.factor(d[,"d3h2"])
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	1394	39.2	42.8
Rarely	1135	31.9	34.9
Sometimes	630	17.7	19.4
Often	92	2.6	2.8
Scantron_Error	3	0.1	0.1
NA	303	8.5	NA
Total	3557	100.0	100.0

#3
  d3h3 <- as.factor(d[,"d3h3"])
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	1089	30.6	33.6
Rarely	944	26.5	29.1
Sometimes	931	26.2	28.7
Often	273	7.7	8.4
Scantron_Error	3	0.1	0.1
NA	317	8.9	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	166	51.7	52.9
Rarely	106	33.0	33.8
Sometimes	38	11.8	12.1
Often	4	1.2	1.3
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	96	29.9	30.9
Rarely	143	44.5	46.0
Sometimes	62	19.3	19.9
Often	10	3.1	3.2
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	69	21.5	22.2
Rarely	115	35.8	37.0
Sometimes	109	34.0	35.0
Often	18	5.6	5.8
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	98	46.7	47.8
Rarely	68	32.4	33.2
Sometimes	34	16.2	16.6
Often	5	2.4	2.4
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	73	34.8	37.1
Rarely	74	35.2	37.6
Sometimes	41	19.5	20.8
Often	9	4.3	4.6
Scantron_Error	0	0.0	0.0
NA	13	6.2	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	58	27.6	29.6
Rarely	57	27.1	29.1
Sometimes	63	30.0	32.1
Often	18	8.6	9.2
Scantron_Error	0	0.0	0.0
NA	14	6.7	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	155	49.2	51.5
Rarely	102	32.4	33.9
Sometimes	37	11.7	12.3
Often	6	1.9	2.0
Scantron_Error	1	0.3	0.3
NA	14	4.4	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	112	35.6	38.4
Rarely	109	34.6	37.3
Sometimes	62	19.7	21.2
Often	8	2.5	2.7
Scantron_Error	1	0.3	0.3
NA	23	7.3	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	79	25.1	27.2
Rarely	81	25.7	27.9
Sometimes	94	29.8	32.4
Often	35	11.1	12.1
Scantron_Error	1	0.3	0.3
NA	25	7.9	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	176	49.4	52.1
Rarely	107	30.1	31.7
Sometimes	50	14.0	14.8
Often	5	1.4	1.5
Scantron_Error	0	0.0	0.0
NA	18	5.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	128	36.0	40.6
Rarely	117	32.9	37.1
Sometimes	59	16.6	18.7
Often	11	3.1	3.5
Scantron_Error	0	0.0	0.0
NA	41	11.5	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	105	29.5	33.5
Rarely	98	27.5	31.3
Sometimes	90	25.3	28.8
Often	20	5.6	6.4
Scantron_Error	0	0.0	0.0
NA	43	12.1	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	317	54.2	56.2
Rarely	149	25.5	26.4
Sometimes	86	14.7	15.2
Often	12	2.1	2.1
Scantron_Error	0	0.0	0.0
NA	21	3.6	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	242	41.4	46.4
Rarely	152	26.0	29.1
Sometimes	108	18.5	20.7
Often	20	3.4	3.8
Scantron_Error	0	0.0	0.0
NA	63	10.8	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	190	32.5	36.5
Rarely	146	25.0	28.0
Sometimes	134	22.9	25.7
Often	51	8.7	9.8
Scantron_Error	0	0.0	0.0
NA	64	10.9	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	984	56.1	58.2
Rarely	466	26.6	27.6
Sometimes	219	12.5	13.0
Often	20	1.1	1.2
Scantron_Error	1	0.1	0.1
NA	64	3.6	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	734	41.8	45.8
Rarely	538	30.7	33.6
Sometimes	295	16.8	18.4
Often	34	1.9	2.1
Scantron_Error	2	0.1	0.1
NA	151	8.6	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	581	33.1	36.4
Rarely	446	25.4	28.0
Sometimes	435	24.8	27.3
Often	131	7.5	8.2
Scantron_Error	2	0.1	0.1
NA	159	9.1	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	9	56.2	60.0
Rarely	4	25.0	26.7
Sometimes	2	12.5	13.3
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	9	56.2	64.3
Rarely	2	12.5	14.3
Sometimes	3	18.8	21.4
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	2	12.5	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	7	43.8	50.0
Rarely	1	6.2	7.1
Sometimes	6	37.5	42.9
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	2	12.5	NA
Total	16	100.0	100.0

rm(temp.dd)

D3I

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. You have been followed around in stores
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3i1 <- as.factor(d[,"d3i1"])
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1294	36.4	37.7
Rarely	952	26.8	27.8
Sometimes	932	26.2	27.2
Often	249	7.0	7.3
Scantron_Error	2	0.1	0.1
NA	128	3.6	NA
Total	3557	100.0	100.0

#2
  d3i2 <- as.factor(d[,"d3i2"])
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	917	25.8	28.2
Rarely	888	25.0	27.3
Sometimes	1111	31.2	34.1
Often	338	9.5	10.4
Scantron_Error	1	0.0	0.0
NA	302	8.5	NA
Total	3557	100.0	100.0

#3
  d3i3 <- as.factor(d[,"d3i3"])
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	773	21.7	23.9
Rarely	635	17.9	19.6
Sometimes	1133	31.9	35.0
Often	692	19.5	21.4
Scantron_Error	3	0.1	0.1
NA	321	9.0	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	116	36.1	36.7
Rarely	80	24.9	25.3
Sometimes	90	28.0	28.5
Often	30	9.3	9.5
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	74	23.1	23.8
Rarely	93	29.0	29.9
Sometimes	108	33.6	34.7
Often	36	11.2	11.6
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	56	17.4	17.9
Rarely	75	23.4	24.0
Sometimes	126	39.3	40.4
Often	55	17.1	17.6
Scantron_Error	0	0.0	0.0
NA	9	2.8	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	53	25.2	25.9
Rarely	61	29.0	29.8
Sometimes	75	35.7	36.6
Often	16	7.6	7.8
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	39	18.6	19.8
Rarely	48	22.9	24.4
Sometimes	82	39.0	41.6
Often	28	13.3	14.2
Scantron_Error	0	0.0	0.0
NA	13	6.2	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	37	17.6	19.0
Rarely	32	15.2	16.4
Sometimes	71	33.8	36.4
Often	55	26.2	28.2
Scantron_Error	0	0.0	0.0
NA	15	7.1	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	95	30.2	31.7
Rarely	95	30.2	31.7
Sometimes	80	25.4	26.7
Often	30	9.5	10.0
Scantron_Error	0	0.0	0.0
NA	15	4.8	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	62	19.7	21.3
Rarely	83	26.3	28.5
Sometimes	102	32.4	35.1
Often	44	14.0	15.1
Scantron_Error	0	0.0	0.0
NA	24	7.6	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	64	20.3	22.0
Rarely	51	16.2	17.5
Sometimes	92	29.2	31.6
Often	84	26.7	28.9
Scantron_Error	0	0.0	0.0
NA	24	7.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	122	34.3	35.6
Rarely	104	29.2	30.3
Sometimes	92	25.8	26.8
Often	25	7.0	7.3
Scantron_Error	0	0.0	0.0
NA	13	3.7	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	88	24.7	27.5
Rarely	94	26.4	29.4
Sometimes	109	30.6	34.1
Often	29	8.1	9.1
Scantron_Error	0	0.0	0.0
NA	36	10.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	73	20.5	23.2
Rarely	77	21.6	24.4
Sometimes	107	30.1	34.0
Often	58	16.3	18.4
Scantron_Error	0	0.0	0.0
NA	41	11.5	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	241	41.2	43.1
Rarely	123	21.0	22.0
Sometimes	152	26.0	27.2
Often	43	7.4	7.7
Scantron_Error	0	0.0	0.0
NA	26	4.4	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	166	28.4	32.4
Rarely	119	20.3	23.2
Sometimes	176	30.1	34.3
Often	52	8.9	10.1
Scantron_Error	0	0.0	0.0
NA	72	12.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	140	23.9	27.3
Rarely	77	13.2	15.0
Sometimes	188	32.1	36.6
Often	108	18.5	21.1
Scantron_Error	0	0.0	0.0
NA	72	12.3	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	661	37.7	39.1
Rarely	483	27.5	28.6
Sometimes	441	25.1	26.1
Often	103	5.9	6.1
Scantron_Error	2	0.1	0.1
NA	64	3.6	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	484	27.6	30.1
Rarely	445	25.4	27.7
Sometimes	532	30.3	33.1
Often	146	8.3	9.1
Scantron_Error	1	0.1	0.1
NA	146	8.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	398	22.7	25.0
Rarely	321	18.3	20.1
Sometimes	543	31.0	34.0
Often	330	18.8	20.7
Scantron_Error	3	0.2	0.2
NA	159	9.1	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	6	37.5	37.5
Rarely	6	37.5	37.5
Sometimes	2	12.5	12.5
Often	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	4	25.0	26.7
Rarely	6	37.5	40.0
Sometimes	2	12.5	13.3
Often	3	18.8	20.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	5	31.2	33.3
Rarely	2	12.5	13.3
Sometimes	6	37.5	40.0
Often	2	12.5	13.3
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D3J

D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
- 1. Current (from prostate cancer diagnosis to the present)
- 1. Age 31 up to just before prostate cancer diagnosis
- 1. Childhood or young adult life (up to age 30)
  - 1=Never
  - 2=Rarely
  - 3=Sometimes
  - 4=Often

All data

# 1
  d3j1 <- as.factor(d[,"d3j1"])
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	1451	40.8	42.4
Rarely	1165	32.8	34.0
Sometimes	602	16.9	17.6
Often	206	5.8	6.0
Scantron_Error	1	0.0	0.0
NA	132	3.7	NA
Total	3557	100.0	100.0

#2
  d3j2 <- as.factor(d[,"d3j2"])
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	1116	31.4	34.4
Rarely	1149	32.3	35.4
Sometimes	737	20.7	22.7
Often	245	6.9	7.5
Scantron_Error	1	0.0	0.0
NA	309	8.7	NA
Total	3557	100.0	100.0

#3
  d3j3 <- as.factor(d[,"d3j3"])
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4",
                     Scantron_Error="*")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often","Scantron_Error"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	995	28.0	30.8
Rarely	1008	28.3	31.2
Sometimes	798	22.4	24.7
Often	428	12.0	13.3
Scantron_Error	0	0.0	0.0
NA	328	9.2	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	91	28.3	28.7
Rarely	107	33.3	33.8
Sometimes	83	25.9	26.2
Often	36	11.2	11.4
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	62	19.3	19.9
Rarely	113	35.2	36.3
Sometimes	94	29.3	30.2
Often	42	13.1	13.5
Scantron_Error	0	0.0	0.0
NA	10	3.1	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	56	17.4	17.9
Rarely	99	30.8	31.6
Sometimes	114	35.5	36.4
Often	44	13.7	14.1
Scantron_Error	0	0.0	0.0
NA	8	2.5	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	72	34.3	35.3
Rarely	66	31.4	32.4
Sometimes	47	22.4	23.0
Often	19	9.0	9.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	58	27.6	29.9
Rarely	57	27.1	29.4
Sometimes	53	25.2	27.3
Often	26	12.4	13.4
Scantron_Error	0	0.0	0.0
NA	16	7.6	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	55	26.2	28.6
Rarely	43	20.5	22.4
Sometimes	65	31.0	33.9
Often	29	13.8	15.1
Scantron_Error	0	0.0	0.0
NA	18	8.6	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	105	33.3	35.1
Rarely	110	34.9	36.8
Sometimes	61	19.4	20.4
Often	23	7.3	7.7
Scantron_Error	0	0.0	0.0
NA	16	5.1	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	78	24.8	26.9
Rarely	106	33.7	36.6
Sometimes	71	22.5	24.5
Often	35	11.1	12.1
Scantron_Error	0	0.0	0.0
NA	25	7.9	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	73	23.2	25.3
Rarely	87	27.6	30.2
Sometimes	65	20.6	22.6
Often	63	20.0	21.9
Scantron_Error	0	0.0	0.0
NA	27	8.6	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	143	40.2	42.3
Rarely	115	32.3	34.0
Sometimes	61	17.1	18.0
Often	19	5.3	5.6
Scantron_Error	0	0.0	0.0
NA	18	5.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	108	30.3	34.0
Rarely	115	32.3	36.2
Sometimes	74	20.8	23.3
Often	21	5.9	6.6
Scantron_Error	0	0.0	0.0
NA	38	10.7	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	97	27.2	31.0
Rarely	110	30.9	35.1
Sometimes	74	20.8	23.6
Often	32	9.0	10.2
Scantron_Error	0	0.0	0.0
NA	43	12.1	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	245	41.9	43.8
Rarely	192	32.8	34.3
Sometimes	87	14.9	15.5
Often	36	6.2	6.4
Scantron_Error	0	0.0	0.0
NA	25	4.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	179	30.6	34.7
Rarely	185	31.6	35.9
Sometimes	110	18.8	21.3
Often	42	7.2	8.1
Scantron_Error	0	0.0	0.0
NA	69	11.8	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	158	27.0	30.6
Rarely	169	28.9	32.7
Sometimes	110	18.8	21.3
Often	80	13.7	15.5
Scantron_Error	0	0.0	0.0
NA	68	11.6	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	785	44.8	46.4
Rarely	569	32.4	33.6
Sometimes	263	15.0	15.6
Often	73	4.2	4.3
Scantron_Error	1	0.1	0.1
NA	63	3.6	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	622	35.5	38.8
Rarely	570	32.5	35.5
Sometimes	333	19.0	20.8
Often	78	4.4	4.9
Scantron_Error	1	0.1	0.1
NA	150	8.6	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	547	31.2	34.4
Rarely	496	28.3	31.2
Sometimes	369	21.0	23.2
Often	179	10.2	11.3
Scantron_Error	0	0.0	0.0
NA	163	9.3	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")

1. Current (from prostate cancer diagnosis to the present)
	n	%	val%
Never	10	62.5	62.5
Rarely	6	37.5	37.5
Sometimes	0	0.0	0.0
Often	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")

2. Age 31 up to just before prostate cancer diagnosis
	n	%	val%
Never	9	56.2	60.0
Rarely	3	18.8	20.0
Sometimes	2	12.5	13.3
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")

3. Childhood or young adult life (up to age 30)
	n	%	val%
Never	9	56.2	60.0
Rarely	4	25.0	26.7
Sometimes	1	6.2	6.7
Often	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

D4

D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
- 1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
- 1. Once you make up your mind to do something, you stay with it until the job is completely done.
- 1. You like doing things that other people thought could not be done.
- 1. When things don’t go the way you want them to, that just makes you work even harder.
- 1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
- 1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
- 1. Very seldom have you been disappointed by the results of your hard work.
- 1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
- 1. In the past, even when things got really tough, you never lost sight of your goals.
- 1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
- 1. You don’t let your personal feelings get in the way of doing a job.
- 1. Hard work has really helped you to get ahead in life.
  - 1=Strongly Agree
  - 2=Somewhat Agree
  - 3=Somewhat Disagree
  - 4=Strongly Disagree

All data

# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	1762	49.5	50.6	49.5	50.6
Somewhat_Agree	1383	38.9	39.8	88.4	90.4
Somewhat_Disagree	269	7.6	7.7	96.0	98.1
Strongly_Disagree	57	1.6	1.6	97.6	99.8
Scantron_Error	8	0.2	0.2	97.8	100.0
NA	78	2.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	2251	63.3	64.5	63.3	64.5
Somewhat_Agree	1092	30.7	31.3	94.0	95.8
Somewhat_Disagree	121	3.4	3.5	97.4	99.2
Strongly_Disagree	19	0.5	0.5	97.9	99.8
Scantron_Error	8	0.2	0.2	98.1	100.0
NA	66	1.9	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	1659	46.6	47.9	46.6	47.9
Somewhat_Agree	1384	38.9	40.0	85.5	87.9
Somewhat_Disagree	351	9.9	10.1	95.4	98.0
Strongly_Disagree	59	1.7	1.7	97.1	99.7
Scantron_Error	9	0.3	0.3	97.3	100.0
NA	95	2.7	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	1695	47.7	48.9	47.7	48.9
Somewhat_Agree	1421	39.9	41.0	87.6	89.9
Somewhat_Disagree	297	8.3	8.6	96.0	98.5
Strongly_Disagree	47	1.3	1.4	97.3	99.9
Scantron_Error	5	0.1	0.1	97.4	100.0
NA	92	2.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	1489	41.9	42.8	41.9	42.8
Somewhat_Agree	1373	38.6	39.5	80.5	82.3
Somewhat_Disagree	506	14.2	14.5	94.7	96.8
Strongly_Disagree	105	3.0	3.0	97.6	99.9
Scantron_Error	5	0.1	0.1	97.8	100.0
NA	79	2.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	2258	63.5	64.8	63.5	64.8
Somewhat_Agree	1126	31.7	32.3	95.1	97.1
Somewhat_Disagree	72	2.0	2.1	97.2	99.2
Strongly_Disagree	22	0.6	0.6	97.8	99.8
Scantron_Error	7	0.2	0.2	98.0	100.0
NA	72	2.0	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	1262	35.5	36.4	35.5	36.4
Somewhat_Agree	1605	45.1	46.3	80.6	82.7
Somewhat_Disagree	458	12.9	13.2	93.5	96.0
Strongly_Disagree	137	3.9	4.0	97.3	99.9
Scantron_Error	3	0.1	0.1	97.4	100.0
NA	92	2.6	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	2179	61.3	62.7	61.3	62.7
Somewhat_Agree	1110	31.2	31.9	92.5	94.6
Somewhat_Disagree	156	4.4	4.5	96.9	99.1
Strongly_Disagree	27	0.8	0.8	97.6	99.9
Scantron_Error	4	0.1	0.1	97.7	100.0
NA	81	2.3	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	1998	56.2	57.4	56.2	57.4
Somewhat_Agree	1231	34.6	35.3	90.8	92.7
Somewhat_Disagree	215	6.0	6.2	96.8	98.9
Strongly_Disagree	31	0.9	0.9	97.7	99.8
Scantron_Error	8	0.2	0.2	97.9	100.0
NA	74	2.1	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	1202	33.8	34.6	33.8	34.6
Somewhat_Agree	1396	39.2	40.1	73.0	74.7
Somewhat_Disagree	734	20.6	21.1	93.7	95.8
Strongly_Disagree	137	3.9	3.9	97.5	99.7
Scantron_Error	10	0.3	0.3	97.8	100.0
NA	78	2.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	1856	52.2	53.3	52.2	53.3
Somewhat_Agree	1309	36.8	37.6	89.0	90.8
Somewhat_Disagree	251	7.1	7.2	96.0	98.0
Strongly_Disagree	63	1.8	1.8	97.8	99.8
Scantron_Error	6	0.2	0.2	98.0	100.0
NA	72	2.0	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4",
                     Scantron_Error="*")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree","Scantron_Error"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	2357	66.3	67.7	66.3	67.7
Somewhat_Agree	887	24.9	25.5	91.2	93.2
Somewhat_Disagree	183	5.1	5.3	96.3	98.4
Strongly_Disagree	52	1.5	1.5	97.8	99.9
Scantron_Error	2	0.1	0.1	97.9	100.0
NA	76	2.1	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	151	47.0	47.5	47.0	47.5
Somewhat_Agree	136	42.4	42.8	89.4	90.3
Somewhat_Disagree	25	7.8	7.9	97.2	98.1
Strongly_Disagree	6	1.9	1.9	99.1	100.0
Scantron_Error	0	0.0	0.0	99.1	100.0
NA	3	0.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	193	60.1	60.3	60.1	60.3
Somewhat_Agree	109	34.0	34.1	94.1	94.4
Somewhat_Disagree	16	5.0	5.0	99.1	99.4
Strongly_Disagree	2	0.6	0.6	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	140	43.6	43.8	43.6	43.8
Somewhat_Agree	140	43.6	43.8	87.2	87.5
Somewhat_Disagree	35	10.9	10.9	98.1	98.4
Strongly_Disagree	4	1.2	1.2	99.4	99.7
Scantron_Error	1	0.3	0.3	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	145	45.2	45.5	45.2	45.5
Somewhat_Agree	142	44.2	44.5	89.4	90.0
Somewhat_Disagree	30	9.3	9.4	98.8	99.4
Strongly_Disagree	2	0.6	0.6	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	140	43.6	43.8	43.6	43.8
Somewhat_Agree	125	38.9	39.1	82.6	82.8
Somewhat_Disagree	47	14.6	14.7	97.2	97.5
Strongly_Disagree	8	2.5	2.5	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	201	62.6	63.2	62.6	63.2
Somewhat_Agree	110	34.3	34.6	96.9	97.8
Somewhat_Disagree	5	1.6	1.6	98.4	99.4
Strongly_Disagree	1	0.3	0.3	98.8	99.7
Scantron_Error	1	0.3	0.3	99.1	100.0
NA	3	0.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	101	31.5	31.7	31.5	31.7
Somewhat_Agree	156	48.6	48.9	80.1	80.6
Somewhat_Disagree	49	15.3	15.4	95.3	95.9
Strongly_Disagree	13	4.0	4.1	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	202	62.9	63.1	62.9	63.1
Somewhat_Agree	103	32.1	32.2	95.0	95.3
Somewhat_Disagree	13	4.0	4.1	99.1	99.4
Strongly_Disagree	2	0.6	0.6	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	171	53.3	53.6	53.3	53.6
Somewhat_Agree	126	39.3	39.5	92.5	93.1
Somewhat_Disagree	21	6.5	6.6	99.1	99.7
Strongly_Disagree	1	0.3	0.3	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	100	31.2	31.2	31.2	31.2
Somewhat_Agree	136	42.4	42.5	73.5	73.8
Somewhat_Disagree	75	23.4	23.4	96.9	97.2
Strongly_Disagree	9	2.8	2.8	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	155	48.3	48.4	48.3	48.4
Somewhat_Agree	131	40.8	40.9	89.1	89.4
Somewhat_Disagree	30	9.3	9.4	98.4	98.8
Strongly_Disagree	4	1.2	1.2	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	213	66.4	66.6	66.4	66.6
Somewhat_Agree	82	25.5	25.6	91.9	92.2
Somewhat_Disagree	21	6.5	6.6	98.4	98.8
Strongly_Disagree	4	1.2	1.2	99.7	100.0
Scantron_Error	0	0.0	0.0	99.7	100.0
NA	1	0.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

Northern CA

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	97	46.2	46.6	46.2	46.6
Somewhat_Agree	87	41.4	41.8	87.6	88.5
Somewhat_Disagree	19	9.0	9.1	96.7	97.6
Strongly_Disagree	5	2.4	2.4	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	123	58.6	59.1	58.6	59.1
Somewhat_Agree	77	36.7	37.0	95.2	96.2
Somewhat_Disagree	7	3.3	3.4	98.6	99.5
Strongly_Disagree	1	0.5	0.5	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	102	48.6	48.8	48.6	48.8
Somewhat_Agree	86	41.0	41.1	89.5	90.0
Somewhat_Disagree	19	9.0	9.1	98.6	99.0
Strongly_Disagree	2	1.0	1.0	99.5	100.0
Scantron_Error	0	0.0	0.0	99.5	100.0
NA	1	0.5	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	89	42.4	42.8	42.4	42.8
Somewhat_Agree	104	49.5	50.0	91.9	92.8
Somewhat_Disagree	14	6.7	6.7	98.6	99.5
Strongly_Disagree	1	0.5	0.5	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	80	38.1	38.3	38.1	38.3
Somewhat_Agree	88	41.9	42.1	80.0	80.4
Somewhat_Disagree	34	16.2	16.3	96.2	96.7
Strongly_Disagree	7	3.3	3.3	99.5	100.0
Scantron_Error	0	0.0	0.0	99.5	100.0
NA	1	0.5	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	135	64.3	64.9	64.3	64.9
Somewhat_Agree	66	31.4	31.7	95.7	96.6
Somewhat_Disagree	4	1.9	1.9	97.6	98.6
Strongly_Disagree	2	1.0	1.0	98.6	99.5
Scantron_Error	1	0.5	0.5	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	63	30.0	30.6	30.0	30.6
Somewhat_Agree	105	50.0	51.0	80.0	81.6
Somewhat_Disagree	30	14.3	14.6	94.3	96.1
Strongly_Disagree	8	3.8	3.9	98.1	100.0
Scantron_Error	0	0.0	0.0	98.1	100.0
NA	4	1.9	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	119	56.7	57.2	56.7	57.2
Somewhat_Agree	75	35.7	36.1	92.4	93.3
Somewhat_Disagree	12	5.7	5.8	98.1	99.0
Strongly_Disagree	2	1.0	1.0	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	114	54.3	54.5	54.3	54.5
Somewhat_Agree	81	38.6	38.8	92.9	93.3
Somewhat_Disagree	11	5.2	5.3	98.1	98.6
Strongly_Disagree	3	1.4	1.4	99.5	100.0
Scantron_Error	0	0.0	0.0	99.5	100.0
NA	1	0.5	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	63	30.0	30.1	30.0	30.1
Somewhat_Agree	87	41.4	41.6	71.4	71.8
Somewhat_Disagree	52	24.8	24.9	96.2	96.7
Strongly_Disagree	7	3.3	3.3	99.5	100.0
Scantron_Error	0	0.0	0.0	99.5	100.0
NA	1	0.5	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	93	44.3	44.7	44.3	44.7
Somewhat_Agree	92	43.8	44.2	88.1	88.9
Somewhat_Disagree	22	10.5	10.6	98.6	99.5
Strongly_Disagree	1	0.5	0.5	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	131	62.4	63.0	62.4	63.0
Somewhat_Agree	63	30.0	30.3	92.4	93.3
Somewhat_Disagree	12	5.7	5.8	98.1	99.0
Strongly_Disagree	2	1.0	1.0	99.0	100.0
Scantron_Error	0	0.0	0.0	99.0	100.0
NA	2	1.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

Greater CA

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	150	47.6	49.5	47.6	49.5
Somewhat_Agree	113	35.9	37.3	83.5	86.8
Somewhat_Disagree	34	10.8	11.2	94.3	98.0
Strongly_Disagree	6	1.9	2.0	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	189	60.0	62.2	60.0	62.2
Somewhat_Agree	92	29.2	30.3	89.2	92.4
Somewhat_Disagree	19	6.0	6.2	95.2	98.7
Strongly_Disagree	3	1.0	1.0	96.2	99.7
Scantron_Error	1	0.3	0.3	96.5	100.0
NA	11	3.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	141	44.8	47.2	44.8	47.2
Somewhat_Agree	125	39.7	41.8	84.4	89.0
Somewhat_Disagree	27	8.6	9.0	93.0	98.0
Strongly_Disagree	6	1.9	2.0	94.9	100.0
Scantron_Error	0	0.0	0.0	94.9	100.0
NA	16	5.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	141	44.8	46.7	44.8	46.7
Somewhat_Agree	133	42.2	44.0	87.0	90.7
Somewhat_Disagree	23	7.3	7.6	94.3	98.3
Strongly_Disagree	5	1.6	1.7	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	115	36.5	38.1	36.5	38.1
Somewhat_Agree	128	40.6	42.4	77.1	80.5
Somewhat_Disagree	49	15.6	16.2	92.7	96.7
Strongly_Disagree	10	3.2	3.3	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	191	60.6	63.0	60.6	63.0
Somewhat_Agree	107	34.0	35.3	94.6	98.3
Somewhat_Disagree	5	1.6	1.7	96.2	100.0
Strongly_Disagree	0	0.0	0.0	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	116	36.8	38.4	36.8	38.4
Somewhat_Agree	133	42.2	44.0	79.0	82.5
Somewhat_Disagree	47	14.9	15.6	94.0	98.0
Strongly_Disagree	6	1.9	2.0	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	179	56.8	59.1	56.8	59.1
Somewhat_Agree	106	33.7	35.0	90.5	94.1
Somewhat_Disagree	15	4.8	5.0	95.2	99.0
Strongly_Disagree	3	1.0	1.0	96.2	100.0
Scantron_Error	0	0.0	0.0	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	174	55.2	57.6	55.2	57.6
Somewhat_Agree	105	33.3	34.8	88.6	92.4
Somewhat_Disagree	22	7.0	7.3	95.6	99.7
Strongly_Disagree	1	0.3	0.3	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	90	28.6	29.7	28.6	29.7
Somewhat_Agree	121	38.4	39.9	67.0	69.6
Somewhat_Disagree	76	24.1	25.1	91.1	94.7
Strongly_Disagree	15	4.8	5.0	95.9	99.7
Scantron_Error	1	0.3	0.3	96.2	100.0
NA	12	3.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	155	49.2	51.3	49.2	51.3
Somewhat_Agree	117	37.1	38.7	86.3	90.1
Somewhat_Disagree	27	8.6	8.9	94.9	99.0
Strongly_Disagree	3	1.0	1.0	95.9	100.0
Scantron_Error	0	0.0	0.0	95.9	100.0
NA	13	4.1	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	199	63.2	65.5	63.2	65.5
Somewhat_Agree	81	25.7	26.6	88.9	92.1
Somewhat_Disagree	19	6.0	6.2	94.9	98.4
Strongly_Disagree	5	1.6	1.6	96.5	100.0
Scantron_Error	0	0.0	0.0	96.5	100.0
NA	11	3.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

Detroit

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	168	47.2	47.6	47.2	47.6
Somewhat_Agree	143	40.2	40.5	87.4	88.1
Somewhat_Disagree	33	9.3	9.3	96.6	97.5
Strongly_Disagree	8	2.2	2.3	98.9	99.7
Scantron_Error	1	0.3	0.3	99.2	100.0
NA	3	0.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	221	62.1	62.4	62.1	62.4
Somewhat_Agree	120	33.7	33.9	95.8	96.3
Somewhat_Disagree	11	3.1	3.1	98.9	99.4
Strongly_Disagree	1	0.3	0.3	99.2	99.7
Scantron_Error	1	0.3	0.3	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	155	43.5	44.5	43.5	44.5
Somewhat_Agree	145	40.7	41.7	84.3	86.2
Somewhat_Disagree	39	11.0	11.2	95.2	97.4
Strongly_Disagree	8	2.2	2.3	97.5	99.7
Scantron_Error	1	0.3	0.3	97.8	100.0
NA	8	2.2	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	144	40.4	41.1	40.4	41.1
Somewhat_Agree	154	43.3	44.0	83.7	85.1
Somewhat_Disagree	44	12.4	12.6	96.1	97.7
Strongly_Disagree	7	2.0	2.0	98.0	99.7
Scantron_Error	1	0.3	0.3	98.3	100.0
NA	6	1.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	146	41.0	41.7	41.0	41.7
Somewhat_Agree	140	39.3	40.0	80.3	81.7
Somewhat_Disagree	52	14.6	14.9	94.9	96.6
Strongly_Disagree	12	3.4	3.4	98.3	100.0
Scantron_Error	0	0.0	0.0	98.3	100.0
NA	6	1.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	207	58.1	58.5	58.1	58.5
Somewhat_Agree	136	38.2	38.4	96.3	96.9
Somewhat_Disagree	11	3.1	3.1	99.4	100.0
Strongly_Disagree	0	0.0	0.0	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	137	38.5	38.9	38.5	38.9
Somewhat_Agree	154	43.3	43.8	81.7	82.7
Somewhat_Disagree	48	13.5	13.6	95.2	96.3
Strongly_Disagree	13	3.7	3.7	98.9	100.0
Scantron_Error	0	0.0	0.0	98.9	100.0
NA	4	1.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	225	63.2	63.6	63.2	63.6
Somewhat_Agree	108	30.3	30.5	93.5	94.1
Somewhat_Disagree	18	5.1	5.1	98.6	99.2
Strongly_Disagree	3	0.8	0.8	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	189	53.1	53.5	53.1	53.5
Somewhat_Agree	136	38.2	38.5	91.3	92.1
Somewhat_Disagree	20	5.6	5.7	96.9	97.7
Strongly_Disagree	7	2.0	2.0	98.9	99.7
Scantron_Error	1	0.3	0.3	99.2	100.0
NA	3	0.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	121	34.0	34.5	34.0	34.5
Somewhat_Agree	143	40.2	40.7	74.2	75.2
Somewhat_Disagree	71	19.9	20.2	94.1	95.4
Strongly_Disagree	15	4.2	4.3	98.3	99.7
Scantron_Error	1	0.3	0.3	98.6	100.0
NA	5	1.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	187	52.5	52.8	52.5	52.8
Somewhat_Agree	133	37.4	37.6	89.9	90.4
Somewhat_Disagree	25	7.0	7.1	96.9	97.5
Strongly_Disagree	8	2.2	2.3	99.2	99.7
Scantron_Error	1	0.3	0.3	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	215	60.4	60.7	60.4	60.7
Somewhat_Agree	106	29.8	29.9	90.2	90.7
Somewhat_Disagree	23	6.5	6.5	96.6	97.2
Strongly_Disagree	10	2.8	2.8	99.4	100.0
Scantron_Error	0	0.0	0.0	99.4	100.0
NA	2	0.6	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

Louisiana

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	293	50.1	51.6	50.1	51.6
Somewhat_Agree	219	37.4	38.6	87.5	90.1
Somewhat_Disagree	46	7.9	8.1	95.4	98.2
Strongly_Disagree	10	1.7	1.8	97.1	100.0
Scantron_Error	0	0.0	0.0	97.1	100.0
NA	17	2.9	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	379	64.8	65.9	64.8	65.9
Somewhat_Agree	171	29.2	29.7	94.0	95.7
Somewhat_Disagree	19	3.2	3.3	97.3	99.0
Strongly_Disagree	6	1.0	1.0	98.3	100.0
Scantron_Error	0	0.0	0.0	98.3	100.0
NA	10	1.7	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	288	49.2	50.4	49.2	50.4
Somewhat_Agree	213	36.4	37.3	85.6	87.7
Somewhat_Disagree	56	9.6	9.8	95.2	97.5
Strongly_Disagree	13	2.2	2.3	97.4	99.8
Scantron_Error	1	0.2	0.2	97.6	100.0
NA	14	2.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	302	51.6	53.4	51.6	53.4
Somewhat_Agree	210	35.9	37.1	87.5	90.5
Somewhat_Disagree	44	7.5	7.8	95.0	98.2
Strongly_Disagree	10	1.7	1.8	96.8	100.0
Scantron_Error	0	0.0	0.0	96.8	100.0
NA	19	3.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	273	46.7	47.6	46.7	47.6
Somewhat_Agree	201	34.4	35.1	81.0	82.7
Somewhat_Disagree	82	14.0	14.3	95.0	97.0
Strongly_Disagree	16	2.7	2.8	97.8	99.8
Scantron_Error	1	0.2	0.2	97.9	100.0
NA	12	2.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	377	64.4	65.9	64.4	65.9
Somewhat_Agree	177	30.3	30.9	94.7	96.9
Somewhat_Disagree	10	1.7	1.7	96.4	98.6
Strongly_Disagree	6	1.0	1.0	97.4	99.7
Scantron_Error	2	0.3	0.3	97.8	100.0
NA	13	2.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	232	39.7	40.8	39.7	40.8
Somewhat_Agree	238	40.7	41.8	80.3	82.6
Somewhat_Disagree	67	11.5	11.8	91.8	94.4
Strongly_Disagree	32	5.5	5.6	97.3	100.0
Scantron_Error	0	0.0	0.0	97.3	100.0
NA	16	2.7	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	368	62.9	64.7	62.9	64.7
Somewhat_Agree	161	27.5	28.3	90.4	93.0
Somewhat_Disagree	32	5.5	5.6	95.9	98.6
Strongly_Disagree	8	1.4	1.4	97.3	100.0
Scantron_Error	0	0.0	0.0	97.3	100.0
NA	16	2.7	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	352	60.2	61.4	60.2	61.4
Somewhat_Agree	178	30.4	31.1	90.6	92.5
Somewhat_Disagree	32	5.5	5.6	96.1	98.1
Strongly_Disagree	9	1.5	1.6	97.6	99.7
Scantron_Error	2	0.3	0.3	97.9	100.0
NA	12	2.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	223	38.1	39.1	38.1	39.1
Somewhat_Agree	222	37.9	38.9	76.1	77.9
Somewhat_Disagree	101	17.3	17.7	93.3	95.6
Strongly_Disagree	23	3.9	4.0	97.3	99.6
Scantron_Error	2	0.3	0.4	97.6	100.0
NA	14	2.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	334	57.1	58.3	57.1	58.3
Somewhat_Agree	183	31.3	31.9	88.4	90.2
Somewhat_Disagree	41	7.0	7.2	95.4	97.4
Strongly_Disagree	15	2.6	2.6	97.9	100.0
Scantron_Error	0	0.0	0.0	97.9	100.0
NA	12	2.1	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	406	69.4	71.4	69.4	71.4
Somewhat_Agree	122	20.9	21.4	90.3	92.8
Somewhat_Disagree	27	4.6	4.7	94.9	97.5
Strongly_Disagree	14	2.4	2.5	97.3	100.0
Scantron_Error	0	0.0	0.0	97.3	100.0
NA	16	2.7	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

Georgia

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	894	51.0	52.2	51.0	52.2
Somewhat_Agree	679	38.7	39.6	89.7	91.8
Somewhat_Disagree	112	6.4	6.5	96.1	98.3
Strongly_Disagree	22	1.3	1.3	97.3	99.6
Scantron_Error	7	0.4	0.4	97.7	100.0
NA	40	2.3	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	1138	64.9	66.4	64.9	66.4
Somewhat_Agree	515	29.4	30.0	94.2	96.4
Somewhat_Disagree	49	2.8	2.9	97.0	99.3
Strongly_Disagree	6	0.3	0.4	97.4	99.6
Scantron_Error	6	0.3	0.4	97.7	100.0
NA	40	2.3	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	828	47.2	48.7	47.2	48.7
Somewhat_Agree	670	38.2	39.4	85.4	88.1
Somewhat_Disagree	170	9.7	10.0	95.1	98.1
Strongly_Disagree	26	1.5	1.5	96.6	99.6
Scantron_Error	6	0.3	0.4	96.9	100.0
NA	54	3.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	868	49.5	50.9	49.5	50.9
Somewhat_Agree	671	38.3	39.4	87.7	90.3
Somewhat_Disagree	140	8.0	8.2	95.7	98.5
Strongly_Disagree	21	1.2	1.2	96.9	99.8
Scantron_Error	4	0.2	0.2	97.1	100.0
NA	50	2.9	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	729	41.6	42.7	41.6	42.7
Somewhat_Agree	684	39.0	40.0	80.6	82.7
Somewhat_Disagree	239	13.6	14.0	94.2	96.7
Strongly_Disagree	52	3.0	3.0	97.1	99.8
Scantron_Error	4	0.2	0.2	97.4	100.0
NA	46	2.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	1139	64.9	66.5	64.9	66.5
Somewhat_Agree	523	29.8	30.5	94.8	97.0
Somewhat_Disagree	36	2.1	2.1	96.8	99.1
Strongly_Disagree	13	0.7	0.8	97.5	99.8
Scantron_Error	3	0.2	0.2	97.7	100.0
NA	40	2.3	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	604	34.4	35.5	34.4	35.5
Somewhat_Agree	813	46.4	47.8	80.8	83.3
Somewhat_Disagree	216	12.3	12.7	93.1	96.0
Strongly_Disagree	65	3.7	3.8	96.8	99.8
Scantron_Error	3	0.2	0.2	97.0	100.0
NA	53	3.0	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	1076	61.3	63.1	61.3	63.1
Somewhat_Agree	551	31.4	32.3	92.8	95.4
Somewhat_Disagree	66	3.8	3.9	96.5	99.2
Strongly_Disagree	9	0.5	0.5	97.0	99.8
Scantron_Error	4	0.2	0.2	97.3	100.0
NA	48	2.7	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	990	56.4	57.9	56.4	57.9
Somewhat_Agree	599	34.2	35.0	90.6	92.9
Somewhat_Disagree	107	6.1	6.3	96.7	99.1
Strongly_Disagree	10	0.6	0.6	97.3	99.7
Scantron_Error	5	0.3	0.3	97.5	100.0
NA	43	2.5	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	597	34.0	34.9	34.0	34.9
Somewhat_Agree	686	39.1	40.1	73.1	75.1
Somewhat_Disagree	352	20.1	20.6	93.2	95.7
Strongly_Disagree	68	3.9	4.0	97.1	99.6
Scantron_Error	6	0.3	0.4	97.4	100.0
NA	45	2.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	925	52.7	54.0	52.7	54.0
Somewhat_Agree	645	36.8	37.7	89.5	91.7
Somewhat_Disagree	106	6.0	6.2	95.6	97.9
Strongly_Disagree	31	1.8	1.8	97.3	99.7
Scantron_Error	5	0.3	0.3	97.6	100.0
NA	42	2.4	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	1185	67.6	69.3	67.6	69.3
Somewhat_Agree	428	24.4	25.0	92.0	94.3
Somewhat_Disagree	78	4.4	4.6	96.4	98.9
Strongly_Disagree	17	1.0	1.0	97.4	99.9
Scantron_Error	2	0.1	0.1	97.5	100.0
NA	44	2.5	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

Michigan

a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
	n	%	val%	%cum	val%cum
Strongly_Agree	9	56.2	60	56.2	60
Somewhat_Agree	6	37.5	40	93.8	100
Somewhat_Disagree	0	0.0	0	93.8	100
Strongly_Disagree	0	0.0	0	93.8	100
Scantron_Error	0	0.0	0	93.8	100
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100	100.0	100

b. Once you make up your mind to do something, you stay with it until the job is completely done.
	n	%	val%	%cum	val%cum
Strongly_Agree	8	50	50	50	50
Somewhat_Agree	8	50	50	100	100
Somewhat_Disagree	0	0	0	100	100
Strongly_Disagree	0	0	0	100	100
Scantron_Error	0	0	0	100	100
Total	16	100	100	100	100

c. You like doing things that other people thought could not be done.
	n	%	val%	%cum	val%cum
Strongly_Agree	5	31.2	33.3	31.2	33.3
Somewhat_Agree	5	31.2	33.3	62.5	66.7
Somewhat_Disagree	5	31.2	33.3	93.8	100.0
Strongly_Disagree	0	0.0	0.0	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

d. When things don’t go the way you want them to, that just makes you work even harder.
	n	%	val%	%cum	val%cum
Strongly_Agree	6	37.5	37.5	37.5	37.5
Somewhat_Agree	7	43.8	43.8	81.2	81.2
Somewhat_Disagree	2	12.5	12.5	93.8	93.8
Strongly_Disagree	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
	n	%	val%	%cum	val%cum
Strongly_Agree	6	37.5	37.5	37.5	37.5
Somewhat_Agree	7	43.8	43.8	81.2	81.2
Somewhat_Disagree	3	18.8	18.8	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
	n	%	val%	%cum	val%cum
Strongly_Agree	8	50.0	50.0	50.0	50.0
Somewhat_Agree	7	43.8	43.8	93.8	93.8
Somewhat_Disagree	1	6.2	6.2	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

g. Very seldom have you been disappointed by the results of your hard work.
	n	%	val%	%cum	val%cum
Strongly_Agree	9	56.2	56.2	56.2	56.2
Somewhat_Agree	6	37.5	37.5	93.8	93.8
Somewhat_Disagree	1	6.2	6.2	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
	n	%	val%	%cum	val%cum
Strongly_Agree	10	62.5	62.5	62.5	62.5
Somewhat_Agree	6	37.5	37.5	100.0	100.0
Somewhat_Disagree	0	0.0	0.0	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

i. In the past, even when things got really tough, you never lost sight of your goals.
	n	%	val%	%cum	val%cum
Strongly_Agree	8	50.0	50.0	50.0	50.0
Somewhat_Agree	6	37.5	37.5	87.5	87.5
Somewhat_Disagree	2	12.5	12.5	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
	n	%	val%	%cum	val%cum
Strongly_Agree	8	50.0	50.0	50.0	50.0
Somewhat_Agree	1	6.2	6.2	56.2	56.2
Somewhat_Disagree	7	43.8	43.8	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

k. You don’t let your personal feelings get in the way of doing a job.
	n	%	val%	%cum	val%cum
Strongly_Agree	7	43.8	43.8	43.8	43.8
Somewhat_Agree	8	50.0	50.0	93.8	93.8
Somewhat_Disagree	0	0.0	0.0	93.8	93.8
Strongly_Disagree	1	6.2	6.2	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

l. Hard work has really helped you to get ahead in life.
	n	%	val%	%cum	val%cum
Strongly_Agree	8	50.0	50.0	50.0	50.0
Somewhat_Agree	5	31.2	31.2	81.2	81.2
Somewhat_Disagree	3	18.8	18.8	100.0	100.0
Strongly_Disagree	0	0.0	0.0	100.0	100.0
Scantron_Error	0	0.0	0.0	100.0	100.0
Total	16	100.0	100.0	100.0	100.0

D5

D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
1. Did you live with anyone who was depressed, mentally ill, or suicidal?
1. Did you live with anyone who was a problem drinker or alcoholic?
1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
1. Were your parents separated or divorced?
1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
- 1=No
- 2=Yes
- 3=Parents not married
- 88=Don’t know/not sure
- 99=Prefer not to answer"

All data

# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	2868	80.6	82.3
Yes	323	9.1	9.3
Dont_know_not_sure	262	7.4	7.5
Prefer_not_to_answer	31	0.9	0.9
Scantron_Error	1	0.0	0.0
NA	72	2.0	NA
Total	3557	100.0	100.0

# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	2362	66.4	67.8
Yes	920	25.9	26.4
Dont_know_not_sure	148	4.2	4.2
Prefer_not_to_answer	53	1.5	1.5
Scantron_Error	1	0.0	0.0
NA	73	2.1	NA
Total	3557	100.0	100.0

# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	2936	82.5	84.4
Yes	343	9.6	9.9
Dont_know_not_sure	162	4.6	4.7
Prefer_not_to_answer	37	1.0	1.1
Scantron_Error	1	0.0	0.0
NA	78	2.2	NA
Total	3557	100.0	100.0

# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")

d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
	n	%	val%
No	3014	84.7	86.4
Yes	382	10.7	11.0
Dont_know_not_sure	52	1.5	1.5
Prefer_not_to_answer	37	1.0	1.1
Scantron_Error	2	0.1	0.1
NA	70	2.0	NA
Total	3557	100.0	100.0

# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")

e. Were your parents separated or divorced?
	n	%	val%
No	2101	59.1	60.4
Yes	979	27.5	28.1
Not_married	286	8.0	8.2
Dont_know_not_sure	49	1.4	1.4
Prefer_not_to_answer	58	1.6	1.7
Scantron_Error	6	0.2	0.2
NA	78	2.2	NA
Total	3557	100.0	100.0

# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	2152	60.5	62.2
Once	230	6.5	6.6
More_than_once	527	14.8	15.2
Dont_know_not_sure	400	11.2	11.6
Prefer_not_to_answer	148	4.2	4.3
Scantron_Error	3	0.1	0.1
NA	97	2.7	NA
Total	3557	100.0	100.0

#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	2560	72.0	74.0
Once	135	3.8	3.9
More_than_once	510	14.3	14.7
Dont_know_not_sure	137	3.9	4.0
Prefer_not_to_answer	116	3.3	3.4
Scantron_Error	2	0.1	0.1
NA	97	2.7	NA
Total	3557	100.0	100.0

# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	2077	58.4	60.0
Once	158	4.4	4.6
More_than_once	864	24.3	25.0
Dont_know_not_sure	252	7.1	7.3
Prefer_not_to_answer	110	3.1	3.2
Scantron_Error	1	0.0	0.0
NA	95	2.7	NA
Total	3557	100.0	100.0

# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	3121	87.7	89.7
Once	150	4.2	4.3
More_than_once	113	3.2	3.2
Dont_know_not_sure	52	1.5	1.5
Prefer_not_to_answer	43	1.2	1.2
Scantron_Error	1	0.0	0.0
NA	77	2.2	NA
Total	3557	100.0	100.0

# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	3181	89.4	91.4
Once	121	3.4	3.5
More_than_once	92	2.6	2.6
Dont_know_not_sure	39	1.1	1.1
Prefer_not_to_answer	45	1.3	1.3
Scantron_Error	1	0.0	0.0
NA	78	2.2	NA
Total	3557	100.0	100.0

# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99",
                     Scantron_Error="*")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer","Scantron_Error"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	3302	92.8	94.9
Once	56	1.6	1.6
More_than_once	53	1.5	1.5
Dont_know_not_sure	30	0.8	0.9
Prefer_not_to_answer	40	1.1	1.1
Scantron_Error	0	0.0	0.0
NA	76	2.1	NA
Total	3557	100.0	100.0

LA County

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	266	82.9	83.4
Yes	26	8.1	8.2
Dont_know_not_sure	25	7.8	7.8
Prefer_not_to_answer	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	218	67.9	68.3
Yes	89	27.7	27.9
Dont_know_not_sure	9	2.8	2.8
Prefer_not_to_answer	3	0.9	0.9
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	263	81.9	82.4
Yes	43	13.4	13.5
Dont_know_not_sure	9	2.8	2.8
Prefer_not_to_answer	4	1.2	1.3
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	265	82.6	83.1
Yes	46	14.3	14.4
Dont_know_not_sure	4	1.2	1.3
Prefer_not_to_answer	4	1.2	1.3
Scantron_Error	0	0.0	0.0
NA	2	0.6	NA
Total	321	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	163	50.8	51.6
Yes	130	40.5	41.1
Not_married	9	2.8	2.8
Dont_know_not_sure	10	3.1	3.2
Prefer_not_to_answer	4	1.2	1.3
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	176	54.8	55.7
Once	26	8.1	8.2
More_than_once	65	20.2	20.6
Dont_know_not_sure	35	10.9	11.1
Prefer_not_to_answer	13	4.0	4.1
Scantron_Error	1	0.3	0.3
NA	5	1.6	NA
Total	321	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	229	71.3	72.5
Once	13	4.0	4.1
More_than_once	53	16.5	16.8
Dont_know_not_sure	10	3.1	3.2
Prefer_not_to_answer	11	3.4	3.5
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	181	56.4	58.2
Once	16	5.0	5.1
More_than_once	89	27.7	28.6
Dont_know_not_sure	18	5.6	5.8
Prefer_not_to_answer	6	1.9	1.9
Scantron_Error	1	0.3	0.3
NA	10	3.1	NA
Total	321	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	274	85.4	86.4
Once	18	5.6	5.7
More_than_once	12	3.7	3.8
Dont_know_not_sure	8	2.5	2.5
Prefer_not_to_answer	5	1.6	1.6
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	284	88.5	89.9
Once	15	4.7	4.7
More_than_once	8	2.5	2.5
Dont_know_not_sure	4	1.2	1.3
Prefer_not_to_answer	5	1.6	1.6
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	296	92.2	93.7
Once	10	3.1	3.2
More_than_once	4	1.2	1.3
Dont_know_not_sure	3	0.9	0.9
Prefer_not_to_answer	3	0.9	0.9
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

Northern CA

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	170	81.0	81.0
Yes	21	10.0	10.0
Dont_know_not_sure	17	8.1	8.1
Prefer_not_to_answer	2	1.0	1.0
Scantron_Error	0	0.0	0.0
Total	210	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	140	66.7	67.0
Yes	55	26.2	26.3
Dont_know_not_sure	9	4.3	4.3
Prefer_not_to_answer	5	2.4	2.4
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	166	79.0	79.4
Yes	32	15.2	15.3
Dont_know_not_sure	8	3.8	3.8
Prefer_not_to_answer	3	1.4	1.4
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	176	83.8	84.2
Yes	28	13.3	13.4
Dont_know_not_sure	4	1.9	1.9
Prefer_not_to_answer	1	0.5	0.5
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	109	51.9	52.2
Yes	75	35.7	35.9
Not_married	20	9.5	9.6
Dont_know_not_sure	1	0.5	0.5
Prefer_not_to_answer	4	1.9	1.9
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	128	61.0	61.2
Once	18	8.6	8.6
More_than_once	33	15.7	15.8
Dont_know_not_sure	19	9.0	9.1
Prefer_not_to_answer	11	5.2	5.3
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	152	72.4	73.1
Once	9	4.3	4.3
More_than_once	35	16.7	16.8
Dont_know_not_sure	3	1.4	1.4
Prefer_not_to_answer	9	4.3	4.3
Scantron_Error	0	0.0	0.0
NA	2	1.0	NA
Total	210	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	113	53.8	54.1
Once	14	6.7	6.7
More_than_once	60	28.6	28.7
Dont_know_not_sure	12	5.7	5.7
Prefer_not_to_answer	10	4.8	4.8
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	183	87.1	87.6
Once	13	6.2	6.2
More_than_once	8	3.8	3.8
Dont_know_not_sure	4	1.9	1.9
Prefer_not_to_answer	1	0.5	0.5
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	189	90.0	90.9
Once	8	3.8	3.8
More_than_once	8	3.8	3.8
Dont_know_not_sure	1	0.5	0.5
Prefer_not_to_answer	1	0.5	0.5
Scantron_Error	1	0.5	0.5
NA	2	1.0	NA
Total	210	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	197	93.8	94.3
Once	3	1.4	1.4
More_than_once	3	1.4	1.4
Dont_know_not_sure	4	1.9	1.9
Prefer_not_to_answer	2	1.0	1.0
Scantron_Error	0	0.0	0.0
NA	1	0.5	NA
Total	210	100.0	100.0

Greater CA

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	241	76.5	79.8
Yes	33	10.5	10.9
Dont_know_not_sure	25	7.9	8.3
Prefer_not_to_answer	2	0.6	0.7
Scantron_Error	1	0.3	0.3
NA	13	4.1	NA
Total	315	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	199	63.2	65.9
Yes	86	27.3	28.5
Dont_know_not_sure	16	5.1	5.3
Prefer_not_to_answer	1	0.3	0.3
Scantron_Error	0	0.0	0.0
NA	13	4.1	NA
Total	315	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	251	79.7	82.8
Yes	40	12.7	13.2
Dont_know_not_sure	11	3.5	3.6
Prefer_not_to_answer	1	0.3	0.3
Scantron_Error	0	0.0	0.0
NA	12	3.8	NA
Total	315	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	259	82.2	85.5
Yes	38	12.1	12.5
Dont_know_not_sure	3	1.0	1.0
Prefer_not_to_answer	2	0.6	0.7
Scantron_Error	1	0.3	0.3
NA	12	3.8	NA
Total	315	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	168	53.3	55.4
Yes	104	33.0	34.3
Not_married	21	6.7	6.9
Dont_know_not_sure	3	1.0	1.0
Prefer_not_to_answer	6	1.9	2.0
Scantron_Error	1	0.3	0.3
NA	12	3.8	NA
Total	315	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	170	54.0	56.1
Once	28	8.9	9.2
More_than_once	64	20.3	21.1
Dont_know_not_sure	29	9.2	9.6
Prefer_not_to_answer	11	3.5	3.6
Scantron_Error	1	0.3	0.3
NA	12	3.8	NA
Total	315	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	207	65.7	68.3
Once	13	4.1	4.3
More_than_once	65	20.6	21.5
Dont_know_not_sure	11	3.5	3.6
Prefer_not_to_answer	6	1.9	2.0
Scantron_Error	1	0.3	0.3
NA	12	3.8	NA
Total	315	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	153	48.6	50.3
Once	21	6.7	6.9
More_than_once	97	30.8	31.9
Dont_know_not_sure	24	7.6	7.9
Prefer_not_to_answer	9	2.9	3.0
Scantron_Error	0	0.0	0.0
NA	11	3.5	NA
Total	315	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	269	85.4	88.5
Once	15	4.8	4.9
More_than_once	12	3.8	3.9
Dont_know_not_sure	5	1.6	1.6
Prefer_not_to_answer	3	1.0	1.0
Scantron_Error	0	0.0	0.0
NA	11	3.5	NA
Total	315	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	280	88.9	91.5
Once	11	3.5	3.6
More_than_once	10	3.2	3.3
Dont_know_not_sure	3	1.0	1.0
Prefer_not_to_answer	2	0.6	0.7
Scantron_Error	0	0.0	0.0
NA	9	2.9	NA
Total	315	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	294	93.3	96.1
Once	6	1.9	2.0
More_than_once	3	1.0	1.0
Dont_know_not_sure	2	0.6	0.7
Prefer_not_to_answer	1	0.3	0.3
Scantron_Error	0	0.0	0.0
NA	9	2.9	NA
Total	315	100.0	100.0

Detroit

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	268	75.3	76.6
Yes	44	12.4	12.6
Dont_know_not_sure	35	9.8	10.0
Prefer_not_to_answer	3	0.8	0.9
Scantron_Error	0	0.0	0.0
NA	6	1.7	NA
Total	356	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	206	57.9	58.9
Yes	122	34.3	34.9
Dont_know_not_sure	19	5.3	5.4
Prefer_not_to_answer	3	0.8	0.9
Scantron_Error	0	0.0	0.0
NA	6	1.7	NA
Total	356	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	269	75.6	77.3
Yes	55	15.4	15.8
Dont_know_not_sure	22	6.2	6.3
Prefer_not_to_answer	2	0.6	0.6
Scantron_Error	0	0.0	0.0
NA	8	2.2	NA
Total	356	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	295	82.9	84.3
Yes	45	12.6	12.9
Dont_know_not_sure	6	1.7	1.7
Prefer_not_to_answer	4	1.1	1.1
Scantron_Error	0	0.0	0.0
NA	6	1.7	NA
Total	356	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	204	57.3	58.8
Yes	103	28.9	29.7
Not_married	27	7.6	7.8
Dont_know_not_sure	4	1.1	1.2
Prefer_not_to_answer	8	2.2	2.3
Scantron_Error	1	0.3	0.3
NA	9	2.5	NA
Total	356	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	214	60.1	62.6
Once	20	5.6	5.8
More_than_once	42	11.8	12.3
Dont_know_not_sure	45	12.6	13.2
Prefer_not_to_answer	21	5.9	6.1
Scantron_Error	0	0.0	0.0
NA	14	3.9	NA
Total	356	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	252	70.8	72.8
Once	15	4.2	4.3
More_than_once	50	14.0	14.5
Dont_know_not_sure	15	4.2	4.3
Prefer_not_to_answer	14	3.9	4.0
Scantron_Error	0	0.0	0.0
NA	10	2.8	NA
Total	356	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	187	52.5	54.4
Once	16	4.5	4.7
More_than_once	104	29.2	30.2
Dont_know_not_sure	27	7.6	7.8
Prefer_not_to_answer	10	2.8	2.9
Scantron_Error	0	0.0	0.0
NA	12	3.4	NA
Total	356	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	305	85.7	88.4
Once	14	3.9	4.1
More_than_once	12	3.4	3.5
Dont_know_not_sure	7	2.0	2.0
Prefer_not_to_answer	7	2.0	2.0
Scantron_Error	0	0.0	0.0
NA	11	3.1	NA
Total	356	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	317	89.0	91.9
Once	7	2.0	2.0
More_than_once	11	3.1	3.2
Dont_know_not_sure	3	0.8	0.9
Prefer_not_to_answer	7	2.0	2.0
Scantron_Error	0	0.0	0.0
NA	11	3.1	NA
Total	356	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	323	90.7	93.4
Once	7	2.0	2.0
More_than_once	5	1.4	1.4
Dont_know_not_sure	4	1.1	1.2
Prefer_not_to_answer	7	2.0	2.0
Scantron_Error	0	0.0	0.0
NA	10	2.8	NA
Total	356	100.0	100.0

Louisiana

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	462	79.0	80.9
Yes	60	10.3	10.5
Dont_know_not_sure	43	7.4	7.5
Prefer_not_to_answer	6	1.0	1.1
Scantron_Error	0	0.0	0.0
NA	14	2.4	NA
Total	585	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	424	72.5	74.6
Yes	109	18.6	19.2
Dont_know_not_sure	27	4.6	4.8
Prefer_not_to_answer	7	1.2	1.2
Scantron_Error	1	0.2	0.2
NA	17	2.9	NA
Total	585	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	474	81.0	83.5
Yes	49	8.4	8.6
Dont_know_not_sure	35	6.0	6.2
Prefer_not_to_answer	9	1.5	1.6
Scantron_Error	1	0.2	0.2
NA	17	2.9	NA
Total	585	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	494	84.4	86.7
Yes	59	10.1	10.4
Dont_know_not_sure	8	1.4	1.4
Prefer_not_to_answer	8	1.4	1.4
Scantron_Error	1	0.2	0.2
NA	15	2.6	NA
Total	585	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	365	62.4	63.6
Yes	147	25.1	25.6
Not_married	48	8.2	8.4
Dont_know_not_sure	7	1.2	1.2
Prefer_not_to_answer	7	1.2	1.2
Scantron_Error	0	0.0	0.0
NA	11	1.9	NA
Total	585	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	383	65.5	67.7
Once	28	4.8	4.9
More_than_once	70	12.0	12.4
Dont_know_not_sure	66	11.3	11.7
Prefer_not_to_answer	19	3.2	3.4
Scantron_Error	0	0.0	0.0
NA	19	3.2	NA
Total	585	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	433	74.0	76.0
Once	17	2.9	3.0
More_than_once	71	12.1	12.5
Dont_know_not_sure	29	5.0	5.1
Prefer_not_to_answer	20	3.4	3.5
Scantron_Error	0	0.0	0.0
NA	15	2.6	NA
Total	585	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	388	66.3	67.8
Once	15	2.6	2.6
More_than_once	114	19.5	19.9
Dont_know_not_sure	43	7.4	7.5
Prefer_not_to_answer	12	2.1	2.1
Scantron_Error	0	0.0	0.0
NA	13	2.2	NA
Total	585	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	535	91.5	93.9
Once	11	1.9	1.9
More_than_once	14	2.4	2.5
Dont_know_not_sure	4	0.7	0.7
Prefer_not_to_answer	6	1.0	1.1
Scantron_Error	0	0.0	0.0
NA	15	2.6	NA
Total	585	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	536	91.6	93.9
Once	12	2.1	2.1
More_than_once	10	1.7	1.8
Dont_know_not_sure	5	0.9	0.9
Prefer_not_to_answer	8	1.4	1.4
Scantron_Error	0	0.0	0.0
NA	14	2.4	NA
Total	585	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	544	93.0	95.4
Once	5	0.9	0.9
More_than_once	10	1.7	1.8
Dont_know_not_sure	4	0.7	0.7
Prefer_not_to_answer	7	1.2	1.2
Scantron_Error	0	0.0	0.0
NA	15	2.6	NA
Total	585	100.0	100.0

Georgia

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	1448	82.6	84.3
Yes	137	7.8	8.0
Dont_know_not_sure	116	6.6	6.8
Prefer_not_to_answer	16	0.9	0.9
Scantron_Error	0	0.0	0.0
NA	37	2.1	NA
Total	1754	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	1165	66.4	67.7
Yes	453	25.8	26.3
Dont_know_not_sure	68	3.9	4.0
Prefer_not_to_answer	34	1.9	2.0
Scantron_Error	0	0.0	0.0
NA	34	1.9	NA
Total	1754	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	1500	85.5	87.4
Yes	121	6.9	7.1
Dont_know_not_sure	77	4.4	4.5
Prefer_not_to_answer	18	1.0	1.0
Scantron_Error	0	0.0	0.0
NA	38	2.2	NA
Total	1754	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	1511	86.1	87.8
Yes	165	9.4	9.6
Dont_know_not_sure	26	1.5	1.5
Prefer_not_to_answer	18	1.0	1.0
Scantron_Error	0	0.0	0.0
NA	34	1.9	NA
Total	1754	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	1082	61.7	63.1
Yes	418	23.8	24.4
Not_married	158	9.0	9.2
Dont_know_not_sure	24	1.4	1.4
Prefer_not_to_answer	29	1.7	1.7
Scantron_Error	4	0.2	0.2
NA	39	2.2	NA
Total	1754	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	1071	61.1	62.7
Once	108	6.2	6.3
More_than_once	251	14.3	14.7
Dont_know_not_sure	204	11.6	11.9
Prefer_not_to_answer	73	4.2	4.3
Scantron_Error	1	0.1	0.1
NA	46	2.6	NA
Total	1754	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	1279	72.9	75.2
Once	68	3.9	4.0
More_than_once	231	13.2	13.6
Dont_know_not_sure	67	3.8	3.9
Prefer_not_to_answer	55	3.1	3.2
Scantron_Error	1	0.1	0.1
NA	53	3.0	NA
Total	1754	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	1049	59.8	61.5
Once	75	4.3	4.4
More_than_once	394	22.5	23.1
Dont_know_not_sure	125	7.1	7.3
Prefer_not_to_answer	63	3.6	3.7
Scantron_Error	0	0.0	0.0
NA	48	2.7	NA
Total	1754	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	1543	88.0	89.8
Once	79	4.5	4.6
More_than_once	54	3.1	3.1
Dont_know_not_sure	21	1.2	1.2
Prefer_not_to_answer	21	1.2	1.2
Scantron_Error	1	0.1	0.1
NA	35	2.0	NA
Total	1754	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	1563	89.1	91.0
Once	68	3.9	4.0
More_than_once	43	2.5	2.5
Dont_know_not_sure	21	1.2	1.2
Prefer_not_to_answer	22	1.3	1.3
Scantron_Error	0	0.0	0.0
NA	37	2.1	NA
Total	1754	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	1635	93.2	95.2
Once	25	1.4	1.5
More_than_once	27	1.5	1.6
Dont_know_not_sure	11	0.6	0.6
Prefer_not_to_answer	20	1.1	1.2
Scantron_Error	0	0.0	0.0
NA	36	2.1	NA
Total	1754	100.0	100.0

Michigan

a. Did you live with anyone who was depressed, mentally ill, or suicidal?
	n	%	val%
No	13	81.2	81.2
Yes	2	12.5	12.5
Dont_know_not_sure	1	6.2	6.2
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

b. Did you live with anyone who was a problem drinker or alcoholic?
	n	%	val%
No	10	62.5	62.5
Yes	6	37.5	37.5
Dont_know_not_sure	0	0.0	0.0
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
	n	%	val%
No	13	81.2	81.2
Yes	3	18.8	18.8
Dont_know_not_sure	0	0.0	0.0
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
	n	%	val%
No	14	87.5	87.5
Yes	1	6.2	6.2
Dont_know_not_sure	1	6.2	6.2
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

e. Were your parents separated or divorced?
	n	%	val%
No	10	62.5	66.7
Yes	2	12.5	13.3
Not_married	3	18.8	20.0
Dont_know_not_sure	0	0.0	0.0
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
	n	%	val%
Never	10	62.5	62.5
Once	2	12.5	12.5
More_than_once	2	12.5	12.5
Dont_know_not_sure	2	12.5	12.5
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
	n	%	val%
Never	8	50.0	50.0
Once	0	0.0	0.0
More_than_once	5	31.2	31.2
Dont_know_not_sure	2	12.5	12.5
Prefer_not_to_answer	1	6.2	6.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
	n	%	val%
Never	6	37.5	37.5
Once	1	6.2	6.2
More_than_once	6	37.5	37.5
Dont_know_not_sure	3	18.8	18.8
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
	n	%	val%
Never	12	75.0	75.0
Once	0	0.0	0.0
More_than_once	1	6.2	6.2
Dont_know_not_sure	3	18.8	18.8
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
	n	%	val%
Never	12	75.0	75.0
Once	0	0.0	0.0
More_than_once	2	12.5	12.5
Dont_know_not_sure	2	12.5	12.5
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
	n	%	val%
Never	13	81.2	81.2
Once	0	0.0	0.0
More_than_once	1	6.2	6.2
Dont_know_not_sure	2	12.5	12.5
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

E1

E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
- E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
- E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
- E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
- E1_4: 1=I had bone pain that I went to see my doctor about
- E1_5: 1=I was fearful I had cancer
- E1_6: 1=Other

All data

# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1",
                     Scantron_Error="*")
  e1_1 <- ordered(e1_1, c("High_PSA_test","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	2771	77.9	100
Scantron_Error	0	0.0	0
NA	786	22.1	NA
Total	3557	100.0	100

#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_2 <- ordered(e1_2, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	940	26.4	100
Scantron_Error	0	0.0	0
NA	2617	73.6	NA
Total	3557	100.0	100

#3
  e1_3 <- as.factor(d[,"e1_3"])
  levels(e1_3) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_3 <- ordered(e1_3, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	658	18.5	100
Scantron_Error	0	0.0	0
NA	2899	81.5	NA
Total	3557	100.0	100

#4
  e1_4 <- as.factor(d[,"e1_4"])
  levels(e1_4) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_4 <- ordered(e1_4, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	68	1.9	100
Scantron_Error	0	0.0	0
NA	3489	98.1	NA
Total	3557	100.0	100

#5
  e1_5 <- as.factor(d[,"e1_5"])
  levels(e1_5) <- list(Digital_rectal_exam="1",
                     Scantron_Error="*")
  e1_5 <- ordered(e1_5, c("Digital_rectal_exam","Scantron_Error"))
  
  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	147	4.1	100
Scantron_Error	0	0.0	0
NA	3410	95.9	NA
Total	3557	100.0	100

#6

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	256	79.8	100
Scantron_Error	0	0.0	0
NA	65	20.2	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	74	23.1	100
Scantron_Error	0	0.0	0
NA	247	76.9	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	75	23.4	100
Scantron_Error	0	0.0	0
NA	246	76.6	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	5	1.6	100
Scantron_Error	0	0.0	0
NA	316	98.4	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	5	1.6	100
Scantron_Error	0	0.0	0
NA	316	98.4	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	172	81.9	100
Scantron_Error	0	0.0	0
NA	38	18.1	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	45	21.4	100
Scantron_Error	0	0.0	0
NA	165	78.6	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	40	19	100
Scantron_Error	0	0	0
NA	170	81	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	2	1	100
Scantron_Error	0	0	0
NA	208	99	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	6	2.9	100
Scantron_Error	0	0.0	0
NA	204	97.1	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	235	74.6	100
Scantron_Error	0	0.0	0
NA	80	25.4	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	84	26.7	100
Scantron_Error	0	0.0	0
NA	231	73.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	57	18.1	100
Scantron_Error	0	0.0	0
NA	258	81.9	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	7	2.2	100
Scantron_Error	0	0.0	0
NA	308	97.8	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	16	5.1	100
Scantron_Error	0	0.0	0
NA	299	94.9	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	260	73	100
Scantron_Error	0	0	0
NA	96	27	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	112	31.5	100
Scantron_Error	0	0.0	0
NA	244	68.5	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	59	16.6	100
Scantron_Error	0	0.0	0
NA	297	83.4	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	7	2	100
Scantron_Error	0	0	0
NA	349	98	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	27	7.6	100
Scantron_Error	0	0.0	0
NA	329	92.4	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	447	76.4	100
Scantron_Error	0	0.0	0
NA	138	23.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	157	26.8	100
Scantron_Error	0	0.0	0
NA	428	73.2	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	112	19.1	100
Scantron_Error	0	0.0	0
NA	473	80.9	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	14	2.4	100
Scantron_Error	0	0.0	0
NA	571	97.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	13	2.2	100
Scantron_Error	0	0.0	0
NA	572	97.8	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	1390	79.2	100
Scantron_Error	0	0.0	0
NA	364	20.8	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	463	26.4	100
Scantron_Error	0	0.0	0
NA	1291	73.6	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	311	17.7	100
Scantron_Error	0	0.0	0
NA	1443	82.3	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	31	1.8	100
Scantron_Error	0	0.0	0
NA	1723	98.2	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	79	4.5	100
Scantron_Error	0	0.0	0
NA	1675	95.5	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")

1. I had a high PSA (‘prostate specific antigen’) test
	n	%	val%
High_PSA_test	11	68.8	100
Scantron_Error	0	0.0	0
NA	5	31.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")

2. My doctor did a digital rectal exam that indicated an abnormality
	n	%	val%
Digital_rectal_exam	5	31.2	100
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")

3. I had urinary, sexual, or bowel problems that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	4	25	100
Scantron_Error	0	0	0
NA	12	75	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")

4. I had bone pain that I went to see my doctor about
	n	%	val%
Digital_rectal_exam	2	12.5	100
Scantron_Error	0	0.0	0
NA	14	87.5	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")

5. I was fearful I had cancer
	n	%	val%
Digital_rectal_exam	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

rm(temp.dd)

E2

E2. Before you were diagnosed with prostate cancer:
- 1. Did you have any previous prostate biopsies that were negative?
  - 2=Yes
  - 1=No
  - 88=Don’t know
- If yes, How many?
  - 1=1
  - 2=2
  - 3=3 or more
- 1. Did you have any previous PSA blood tests that were considered normal?
  - 2=Yes
  - 1=No
  - 88=Don’t know
- If yes, How many?
  - 1=1
  - 2=2
  - 3=3
  - 4=4
  - 5=5 or more

All data

  e2aa <- as.factor(d[,"e2aa"])
  levels(e2aa) <- list(No="1",
                       Yes="2",
                     Scantron_Error="*")
  e2aa <- ordered(e2aa, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "biopsies negative")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	459	12.9	15.1
No	2581	72.6	84.8
Scantron_Error	3	0.1	0.1
NA	514	14.5	NA
Total	3557	100.0	100.0

  e2ab <- as.factor(d[,"e2ab"])
  levels(e2ab) <- list(One="1",
                       Two="2",
                       Three_more="3",
                     Scantron_Error="*")
  e2ab <- ordered(e2ab, c("One","Two","Three_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "biopsies negative-number")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	253	7.1	48.2	7.1	48.2
Two	148	4.2	28.2	11.3	76.4
Three_more	124	3.5	23.6	14.8	100.0
Scantron_Error	0	0.0	0.0	14.8	100.0
NA	3032	85.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  e2ba <- as.factor(d[,"e2ba"])
  levels(e2ba) <- list(No="1",
                       Yes="2",
                     Scantron_Error="*")
  e2ba <- ordered(e2ba, c("Yes","No","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "Normal PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	1555	43.7	63.8
No	864	24.3	35.5
Scantron_Error	17	0.5	0.7
NA	1121	31.5	NA
Total	3557	100.0	100.0

  e2bb <- as.factor(d[,"e2bb"])
  levels(e2bb) <- list(One="1",
                       Two="2",
                       Three_more="3",
                     Scantron_Error="*")
  e2bb <- ordered(e2bb, c("One","Two","Three_more","Scantron_Error"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "Normal PSA blood tests-number")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	170	4.8	23.9	4.8	23.9
Two	268	7.5	37.7	12.3	61.6
Three_more	272	7.6	38.3	20.0	99.9
Scantron_Error	1	0.0	0.1	20.0	100.0
NA	2846	80.0	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	44	13.7	14.8
No	254	79.1	85.2
Scantron_Error	0	0.0	0.0
NA	23	7.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	24	7.5	49.0	7.5	49.0
Two	11	3.4	22.4	10.9	71.4
Three_more	14	4.4	28.6	15.3	100.0
Scantron_Error	0	0.0	0.0	15.3	100.0
NA	272	84.7	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	177	55.1	71.1
No	69	21.5	27.7
Scantron_Error	3	0.9	1.2
NA	72	22.4	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	13	4.0	21.7	4.0	21.7
Two	19	5.9	31.7	10.0	53.3
Three_more	28	8.7	46.7	18.7	100.0
Scantron_Error	0	0.0	0.0	18.7	100.0
NA	261	81.3	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	30	14.3	15.9
No	159	75.7	84.1
Scantron_Error	0	0.0	0.0
NA	21	10.0	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	17	8.1	53.1	8.1	53.1
Two	9	4.3	28.1	12.4	81.2
Three_more	6	2.9	18.8	15.2	100.0
Scantron_Error	0	0.0	0.0	15.2	100.0
NA	178	84.8	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	105	50.0	70
No	45	21.4	30
Scantron_Error	0	0.0	0
NA	60	28.6	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	13	6.2	28.3	6.2	28.3
Two	20	9.5	43.5	15.7	71.7
Three_more	13	6.2	28.3	21.9	100.0
Scantron_Error	0	0.0	0.0	21.9	100.0
NA	164	78.1	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	45	14.3	16.5
No	228	72.4	83.5
Scantron_Error	0	0.0	0.0
NA	42	13.3	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	21	6.7	43.8	6.7	43.8
Two	17	5.4	35.4	12.1	79.2
Three_more	10	3.2	20.8	15.2	100.0
Scantron_Error	0	0.0	0.0	15.2	100.0
NA	267	84.8	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	133	42.2	58.1
No	96	30.5	41.9
Scantron_Error	0	0.0	0.0
NA	86	27.3	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	20	6.3	31.7	6.3	31.7
Two	18	5.7	28.6	12.1	60.3
Three_more	25	7.9	39.7	20.0	100.0
Scantron_Error	0	0.0	0.0	20.0	100.0
NA	252	80.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	42	11.8	13.8
No	263	73.9	86.2
Scantron_Error	0	0.0	0.0
NA	51	14.3	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	23	6.5	43.4	6.5	43.4
Two	18	5.1	34.0	11.5	77.4
Three_more	12	3.4	22.6	14.9	100.0
Scantron_Error	0	0.0	0.0	14.9	100.0
NA	303	85.1	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	141	39.6	59.2
No	93	26.1	39.1
Scantron_Error	4	1.1	1.7
NA	118	33.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	19	5.3	24.1	5.3	24.1
Two	30	8.4	38.0	13.8	62.0
Three_more	30	8.4	38.0	22.2	100.0
Scantron_Error	0	0.0	0.0	22.2	100.0
NA	277	77.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	60	10.3	12.6
No	416	71.1	87.0
Scantron_Error	2	0.3	0.4
NA	107	18.3	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	35	6.0	40.7	6.0	40.7
Two	25	4.3	29.1	10.3	69.8
Three_more	26	4.4	30.2	14.7	100.0
Scantron_Error	0	0.0	0.0	14.7	100.0
NA	499	85.3	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	233	39.8	59.1
No	159	27.2	40.4
Scantron_Error	2	0.3	0.5
NA	191	32.6	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	33	5.6	29.5	5.6	29.5
Two	35	6.0	31.2	11.6	60.7
Three_more	44	7.5	39.3	19.1	100.0
Scantron_Error	0	0.0	0.0	19.1	100.0
NA	473	80.9	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	235	13.4	15.8
No	1249	71.2	84.1
Scantron_Error	1	0.1	0.1
NA	269	15.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	130	7.4	51.4	7.4	51.4
Two	67	3.8	26.5	11.2	77.9
Three_more	56	3.2	22.1	14.4	100.0
Scantron_Error	0	0.0	0.0	14.4	100.0
NA	1501	85.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	757	43.2	65.1
No	398	22.7	34.2
Scantron_Error	8	0.5	0.7
NA	591	33.7	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	71	4.0	20.3	4.0	20.3
Two	145	8.3	41.5	12.3	61.9
Three_more	132	7.5	37.8	19.8	99.7
Scantron_Error	1	0.1	0.3	19.9	100.0
NA	1405	80.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")

a. Did you have any previous prostate biopsies that were negative?
	n	%	val%
Yes	3	18.8	20
No	12	75.0	80
Scantron_Error	0	0.0	0
NA	1	6.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e2ab,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How many?")

a. If yes, How many?
	n	%	val%	%cum	val%cum
One	3	18.8	75	18.8	75
Two	1	6.2	25	25.0	100
Three_more	0	0.0	0	25.0	100
Scantron_Error	0	0.0	0	25.0	100
NA	12	75.0	NA	100.0	NA
Total	16	100.0	100	100.0	100

  result<-questionr::freq(temp.dd$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")

b. Did you have any previous PSA blood tests that were considered normal?
	n	%	val%
Yes	9	56.2	69.2
No	4	25.0	30.8
Scantron_Error	0	0.0	0.0
NA	3	18.8	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e2bb,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How many?")

b. If yes, How many?
	n	%	val%	%cum	val%cum
One	1	6.2	50	6.2	50
Two	1	6.2	50	12.5	100
Three_more	0	0.0	0	12.5	100
Scantron_Error	0	0.0	0	12.5	100
NA	14	87.5	NA	100.0	NA
Total	16	100.0	100	100.0	100

rm(temp.dd)

E3

E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4= I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor, nurse, or health care provider made the decision
- 88=I do not know or remember how the decision was made

All data

  e3 <- as.factor(d[,"e3"])
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	521	14.6	15.0
With_family_or_friends	230	6.5	6.6
With_family_and_doctor	494	13.9	14.2
With_doctor	878	24.7	25.3
Doctor_made	1073	30.2	30.9
Dont_know_or_remember	204	5.7	5.9
Scantron_Error	75	2.1	2.2
NA	82	2.3	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	68	21.2	21.3
With_family_or_friends	13	4.0	4.1
With_family_and_doctor	34	10.6	10.7
With_doctor	85	26.5	26.6
Doctor_made	105	32.7	32.9
Dont_know_or_remember	12	3.7	3.8
Scantron_Error	2	0.6	0.6
NA	2	0.6	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	34	16.2	16.5
With_family_or_friends	13	6.2	6.3
With_family_and_doctor	28	13.3	13.6
With_doctor	56	26.7	27.2
Doctor_made	64	30.5	31.1
Dont_know_or_remember	5	2.4	2.4
Scantron_Error	6	2.9	2.9
NA	4	1.9	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	46	14.6	14.9
With_family_or_friends	19	6.0	6.2
With_family_and_doctor	41	13.0	13.3
With_doctor	78	24.8	25.3
Doctor_made	112	35.6	36.4
Dont_know_or_remember	7	2.2	2.3
Scantron_Error	5	1.6	1.6
NA	7	2.2	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	54	15.2	15.6
With_family_or_friends	23	6.5	6.6
With_family_and_doctor	51	14.3	14.7
With_doctor	78	21.9	22.5
Doctor_made	109	30.6	31.5
Dont_know_or_remember	23	6.5	6.6
Scantron_Error	8	2.2	2.3
NA	10	2.8	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	74	12.6	12.9
With_family_or_friends	51	8.7	8.9
With_family_and_doctor	80	13.7	14.0
With_doctor	133	22.7	23.3
Doctor_made	183	31.3	32.0
Dont_know_or_remember	39	6.7	6.8
Scantron_Error	12	2.1	2.1
NA	13	2.2	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	241	13.7	14.1
With_family_or_friends	109	6.2	6.4
With_family_and_doctor	259	14.8	15.2
With_doctor	445	25.4	26.1
Doctor_made	495	28.2	29.0
Dont_know_or_remember	118	6.7	6.9
Scantron_Error	41	2.3	2.4
NA	46	2.6	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")

E3
	n	%	val%
Alone	4	25.0	25.0
With_family_or_friends	2	12.5	12.5
With_family_and_doctor	1	6.2	6.2
With_doctor	3	18.8	18.8
Doctor_made	5	31.2	31.2
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	1	6.2	6.2
Total	16	100.0	100.0

rm(temp.dd)

E4

E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
- 1=Low risk of progression
- 2=Intermediate risk of progression
- 3=High risk of progression
- 4=Unknown risk of progression
- 88=Don’t know/Don’t remember

All data

  e4 <- as.factor(d[,"e4"])
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	1280	36.0	36.7
Intermediate_risk	594	16.7	17.0
High_risk	787	22.1	22.5
Unknown_risk	335	9.4	9.6
Dont_know_or_remember	488	13.7	14.0
Scantron_Error	7	0.2	0.2
NA	66	1.9	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	128	39.9	40.4
Intermediate_risk	50	15.6	15.8
High_risk	73	22.7	23.0
Unknown_risk	31	9.7	9.8
Dont_know_or_remember	35	10.9	11.0
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	88	41.9	42.1
Intermediate_risk	51	24.3	24.4
High_risk	39	18.6	18.7
Unknown_risk	13	6.2	6.2
Dont_know_or_remember	17	8.1	8.1
Scantron_Error	1	0.5	0.5
NA	1	0.5	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	97	30.8	31.5
Intermediate_risk	58	18.4	18.8
High_risk	102	32.4	33.1
Unknown_risk	27	8.6	8.8
Dont_know_or_remember	22	7.0	7.1
Scantron_Error	2	0.6	0.6
NA	7	2.2	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	113	31.7	32.1
Intermediate_risk	52	14.6	14.8
High_risk	92	25.8	26.1
Unknown_risk	51	14.3	14.5
Dont_know_or_remember	44	12.4	12.5
Scantron_Error	0	0.0	0.0
NA	4	1.1	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	202	34.5	34.9
Intermediate_risk	91	15.6	15.7
High_risk	114	19.5	19.7
Unknown_risk	65	11.1	11.2
Dont_know_or_remember	105	17.9	18.2
Scantron_Error	1	0.2	0.2
NA	7	1.2	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	647	36.9	37.8
Intermediate_risk	290	16.5	16.9
High_risk	363	20.7	21.2
Unknown_risk	145	8.3	8.5
Dont_know_or_remember	263	15.0	15.4
Scantron_Error	3	0.2	0.2
NA	43	2.5	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")

e4
	n	%	val%
Low_risk	5	31.2	31.2
Intermediate_risk	2	12.5	12.5
High_risk	4	25.0	25.0
Unknown_risk	3	18.8	18.8
Dont_know_or_remember	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

rm(temp.dd)

E5

E5. What was your Gleason score when you were diagnosed with prostate cancer?
- 1=6 or less
- 2=7
- 3=8-10
- 88=Don’t know

All data

  e5 <- as.factor(d[,"e5"])
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88",
                     Scantron_Error="*")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	689	19.4	20.1
Seven	580	16.3	16.9
Eight_to_ten	516	14.5	15.0
Dont_know	1644	46.2	47.9
Scantron_Error	6	0.2	0.2
NA	122	3.4	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	57	17.8	18.1
Seven	52	16.2	16.5
Eight_to_ten	62	19.3	19.7
Dont_know	144	44.9	45.7
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	48	22.9	23.4
Seven	43	20.5	21.0
Eight_to_ten	29	13.8	14.1
Dont_know	84	40.0	41.0
Scantron_Error	1	0.5	0.5
NA	5	2.4	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	67	21.3	22.1
Seven	61	19.4	20.1
Eight_to_ten	57	18.1	18.8
Dont_know	118	37.5	38.9
Scantron_Error	0	0.0	0.0
NA	12	3.8	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	53	14.9	15.3
Seven	63	17.7	18.2
Eight_to_ten	47	13.2	13.6
Dont_know	183	51.4	52.9
Scantron_Error	0	0.0	0.0
NA	10	2.8	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	95	16.2	16.8
Seven	88	15.0	15.6
Eight_to_ten	63	10.8	11.2
Dont_know	317	54.2	56.2
Scantron_Error	1	0.2	0.2
NA	21	3.6	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	364	20.8	21.6
Seven	271	15.5	16.1
Eight_to_ten	255	14.5	15.1
Dont_know	793	45.2	47.0
Scantron_Error	4	0.2	0.2
NA	67	3.8	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")

e5
	n	%	val%
Six_less	5	31.2	33.3
Seven	2	12.5	13.3
Eight_to_ten	3	18.8	20.0
Dont_know	5	31.2	33.3
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

E6

E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
- 1=Localized, confined to prostate
- 2=Regional, tumor extended to regions around the prostate
- 3=Distant, tumor extended to bones or other parts of body
- 88=Don’t know about the stage

All data

  e6 <- as.factor(d[,"e6"])
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88",
                     Scantron_Error="*")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	2451	68.9	71.0
Regional	149	4.2	4.3
Distant	67	1.9	1.9
Dont_know	782	22.0	22.7
Scantron_Error	3	0.1	0.1
NA	105	3.0	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	236	73.5	74.7
Regional	17	5.3	5.4
Distant	5	1.6	1.6
Dont_know	58	18.1	18.4
Scantron_Error	0	0.0	0.0
NA	5	1.6	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	167	79.5	81.1
Regional	2	1.0	1.0
Distant	7	3.3	3.4
Dont_know	30	14.3	14.6
Scantron_Error	0	0.0	0.0
NA	4	1.9	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	229	72.7	75.1
Regional	19	6.0	6.2
Distant	9	2.9	3.0
Dont_know	48	15.2	15.7
Scantron_Error	0	0.0	0.0
NA	10	3.2	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	222	62.4	64.7
Regional	18	5.1	5.2
Distant	11	3.1	3.2
Dont_know	91	25.6	26.5
Scantron_Error	1	0.3	0.3
NA	13	3.7	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	378	64.6	66.5
Regional	20	3.4	3.5
Distant	5	0.9	0.9
Dont_know	165	28.2	29.0
Scantron_Error	0	0.0	0.0
NA	17	2.9	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	1208	68.9	71.1
Regional	72	4.1	4.2
Distant	30	1.7	1.8
Dont_know	386	22.0	22.7
Scantron_Error	2	0.1	0.1
NA	56	3.2	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")

e6
	n	%	val%
Localized	11	68.8	68.8
Regional	1	6.2	6.2
Distant	0	0.0	0.0
Dont_know	4	25.0	25.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

rm(temp.dd)

E7

E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
- 2=Yes
- 1=No
- 88=Don’t Know

All data

  e7 <- as.factor(d[,"e7"])
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e7 <- ordered(e7, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	1280	36.0	37.2
Yes	1328	37.3	38.6
Dont_know	826	23.2	24.0
Scantron_Error	5	0.1	0.1
NA	118	3.3	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	143	44.5	45.4
Yes	116	36.1	36.8
Dont_know	56	17.4	17.8
Scantron_Error	0	0.0	0.0
NA	6	1.9	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	86	41.0	41.5
Yes	90	42.9	43.5
Dont_know	31	14.8	15.0
Scantron_Error	0	0.0	0.0
NA	3	1.4	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	148	47.0	49.2
Yes	116	36.8	38.5
Dont_know	37	11.7	12.3
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	120	33.7	35.1
Yes	121	34.0	35.4
Dont_know	100	28.1	29.2
Scantron_Error	1	0.3	0.3
NA	14	3.9	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	173	29.6	30.6
Yes	235	40.2	41.6
Dont_know	156	26.7	27.6
Scantron_Error	1	0.2	0.2
NA	20	3.4	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	600	34.2	35.4
Yes	646	36.8	38.2
Dont_know	444	25.3	26.2
Scantron_Error	3	0.2	0.2
NA	61	3.5	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")

e7
	n	%	val%
No	10	62.5	62.5
Yes	4	25.0	25.0
Dont_know	2	12.5	12.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

rm(temp.dd)

E8

E8. How did you make your treatment decision?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4=I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor , nurse, or health care provider made the decision
- 6=I don’t know or remember how the decision was made

All data

  e8 <- as.factor(d[,"e8"])
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88",
                     Scantron_Error="*")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember","Scantron_Error"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	466	13.1	13.7
With_family_or_friends	506	14.2	14.8
With_family_and_doctor	1278	35.9	37.5
With_doctor	802	22.5	23.5
Doctor_made	310	8.7	9.1
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	49	1.4	1.4
NA	146	4.1	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	47	14.6	15.1
With_family_or_friends	39	12.1	12.5
With_family_and_doctor	116	36.1	37.2
With_doctor	72	22.4	23.1
Doctor_made	34	10.6	10.9
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	4	1.2	1.3
NA	9	2.8	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	23	11.0	11.2
With_family_or_friends	28	13.3	13.7
With_family_and_doctor	86	41.0	42.0
With_doctor	49	23.3	23.9
Doctor_made	16	7.6	7.8
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	3	1.4	1.5
NA	5	2.4	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	30	9.5	9.8
With_family_or_friends	43	13.7	14.1
With_family_and_doctor	112	35.6	36.7
With_doctor	82	26.0	26.9
Doctor_made	31	9.8	10.2
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	7	2.2	2.3
NA	10	3.2	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	56	15.7	16.7
With_family_or_friends	50	14.0	14.9
With_family_and_doctor	115	32.3	34.3
With_doctor	72	20.2	21.5
Doctor_made	36	10.1	10.7
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	6	1.7	1.8
NA	21	5.9	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	102	17.4	18.2
With_family_or_friends	82	14.0	14.6
With_family_and_doctor	188	32.1	33.6
With_doctor	121	20.7	21.6
Doctor_made	63	10.8	11.2
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	4	0.7	0.7
NA	25	4.3	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	206	11.7	12.3
With_family_or_friends	263	15.0	15.7
With_family_and_doctor	657	37.5	39.1
With_doctor	400	22.8	23.8
Doctor_made	130	7.4	7.7
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	24	1.4	1.4
NA	74	4.2	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")

e8
	n	%	val%
Alone	2	12.5	14.3
With_family_or_friends	1	6.2	7.1
With_family_and_doctor	4	25.0	28.6
With_doctor	6	37.5	42.9
Doctor_made	0	0.0	0.0
Dont_know_or_remember	0	0.0	0.0
Scantron_Error	1	6.2	7.1
NA	2	12.5	NA
Total	16	100.0	100.0

rm(temp.dd)

E9

E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
- E9_1: 1=Best chance for cure of my cancer
- E9_2: 1=Minimize side effects related to sexual function
- E9_3: 1=Minimize side effects related to urinary function
- E9_4: 1=Minimize side effects related to bowel function
- E9_5: 1=Minimize financial cost
- E9_6: 1=Amount of time and travel required to receive treatments
- E9_7: 1=Length of recovery time
- E9_8: 1=Amount of time away from work
- E9_9: 1=Burden on family members
- E9_10: 1=Reduce worry and concern about cancer

All data

  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	3064	86.1	100
Scantron_Error	0	0.0	0
NA	493	13.9	NA
Total	3557	100.0	100

  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	1041	29.3	100
Scantron_Error	0	0.0	0
NA	2516	70.7	NA
Total	3557	100.0	100

  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	933	26.2	100
Scantron_Error	0	0.0	0
NA	2624	73.8	NA
Total	3557	100.0	100

  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	447	12.6	100
Scantron_Error	0	0.0	0
NA	3110	87.4	NA
Total	3557	100.0	100

  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	213	6	100
Scantron_Error	0	0	0
NA	3344	94	NA
Total	3557	100	100

  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	376	10.6	100
Scantron_Error	0	0.0	0
NA	3181	89.4	NA
Total	3557	100.0	100

  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	678	19.1	100
Scantron_Error	0	0.0	0
NA	2879	80.9	NA
Total	3557	100.0	100

  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	284	8	100
Scantron_Error	0	0	0
NA	3273	92	NA
Total	3557	100	100

  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	1518	42.7	100
Scantron_Error	0	0.0	0
NA	2039	57.3	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	294	91.6	100
Scantron_Error	0	0.0	0
NA	27	8.4	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	94	29.3	100
Scantron_Error	0	0.0	0
NA	227	70.7	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	86	26.8	100
Scantron_Error	0	0.0	0
NA	235	73.2	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	31	9.7	100
Scantron_Error	0	0.0	0
NA	290	90.3	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	12	3.7	100
Scantron_Error	0	0.0	0
NA	309	96.3	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	22	6.9	100
Scantron_Error	0	0.0	0
NA	299	93.1	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	45	14	100
Scantron_Error	0	0	0
NA	276	86	NA
Total	321	100	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	23	7.2	100
Scantron_Error	0	0.0	0
NA	298	92.8	NA
Total	321	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	172	53.6	100
Scantron_Error	0	0.0	0
NA	149	46.4	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	185	88.1	100
Scantron_Error	0	0.0	0
NA	25	11.9	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	71	33.8	100
Scantron_Error	0	0.0	0
NA	139	66.2	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	64	30.5	100
Scantron_Error	0	0.0	0
NA	146	69.5	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	38	18.1	100
Scantron_Error	0	0.0	0
NA	172	81.9	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	8	3.8	100
Scantron_Error	0	0.0	0
NA	202	96.2	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	18	8.6	100
Scantron_Error	0	0.0	0
NA	192	91.4	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	40	19	100
Scantron_Error	0	0	0
NA	170	81	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	16	7.6	100
Scantron_Error	0	0.0	0
NA	194	92.4	NA
Total	210	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	81	38.6	100
Scantron_Error	0	0.0	0
NA	129	61.4	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	266	84.4	100
Scantron_Error	0	0.0	0
NA	49	15.6	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	100	31.7	100
Scantron_Error	0	0.0	0
NA	215	68.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	90	28.6	100
Scantron_Error	0	0.0	0
NA	225	71.4	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	44	14	100
Scantron_Error	0	0	0
NA	271	86	NA
Total	315	100	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	11	3.5	100
Scantron_Error	0	0.0	0
NA	304	96.5	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	24	7.6	100
Scantron_Error	0	0.0	0
NA	291	92.4	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	56	17.8	100
Scantron_Error	0	0.0	0
NA	259	82.2	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	27	8.6	100
Scantron_Error	0	0.0	0
NA	288	91.4	NA
Total	315	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	152	48.3	100
Scantron_Error	0	0.0	0
NA	163	51.7	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	303	85.1	100
Scantron_Error	0	0.0	0
NA	53	14.9	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	106	29.8	100
Scantron_Error	0	0.0	0
NA	250	70.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	86	24.2	100
Scantron_Error	0	0.0	0
NA	270	75.8	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	45	12.6	100
Scantron_Error	0	0.0	0
NA	311	87.4	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	22	6.2	100
Scantron_Error	0	0.0	0
NA	334	93.8	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	35	9.8	100
Scantron_Error	0	0.0	0
NA	321	90.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	68	19.1	100
Scantron_Error	0	0.0	0
NA	288	80.9	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	27	7.6	100
Scantron_Error	0	0.0	0
NA	329	92.4	NA
Total	356	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	140	39.3	100
Scantron_Error	0	0.0	0
NA	216	60.7	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	506	86.5	100
Scantron_Error	0	0.0	0
NA	79	13.5	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	124	21.2	100
Scantron_Error	0	0.0	0
NA	461	78.8	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	123	21	100
Scantron_Error	0	0	0
NA	462	79	NA
Total	585	100	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	49	8.4	100
Scantron_Error	0	0.0	0
NA	536	91.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	42	7.2	100
Scantron_Error	0	0.0	0
NA	543	92.8	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	63	10.8	100
Scantron_Error	0	0.0	0
NA	522	89.2	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	98	16.8	100
Scantron_Error	0	0.0	0
NA	487	83.2	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	41	7	100
Scantron_Error	0	0	0
NA	544	93	NA
Total	585	100	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	232	39.7	100
Scantron_Error	0	0.0	0
NA	353	60.3	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	1497	85.3	100
Scantron_Error	0	0.0	0
NA	257	14.7	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	538	30.7	100
Scantron_Error	0	0.0	0
NA	1216	69.3	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	476	27.1	100
Scantron_Error	0	0.0	0
NA	1278	72.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	236	13.5	100
Scantron_Error	0	0.0	0
NA	1518	86.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	117	6.7	100
Scantron_Error	0	0.0	0
NA	1637	93.3	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	213	12.1	100
Scantron_Error	0	0.0	0
NA	1541	87.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	366	20.9	100
Scantron_Error	0	0.0	0
NA	1388	79.1	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	147	8.4	100
Scantron_Error	0	0.0	0
NA	1607	91.6	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	731	41.7	100
Scantron_Error	0	0.0	0
NA	1023	58.3	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")

1. Best chance for cure of my cancer
	n	%	val%
Best_for_cure	13	81.2	100
Scantron_Error	0	0.0	0
NA	3	18.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")

2. Minimize side effects related to sexual function
	n	%	val%
side_effects_sexual	8	50	100
Scantron_Error	0	0	0
NA	8	50	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")

3. Minimize side effects related to urinary function
	n	%	val%
side_effects_urinary	8	50	100
Scantron_Error	0	0	0
NA	8	50	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")

4. Minimize side effects related to bowel function
	n	%	val%
side_effects_bowel	4	25	100
Scantron_Error	0	0	0
NA	12	75	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")

5. Minimize financial cost
	n	%	val%
financial_cost	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")

6. Amount of time and travel required to receive treatments
	n	%	val%
time_and_travel	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")

7. Length of recovery time
	n	%	val%
recovery_time	5	31.2	100
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")

8. Amount of time away from work
	n	%	val%
time_away_from_work	3	18.8	100
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")

9. Burden on family members
	n	%	val%
family_burden	515	14.5	100
Scantron_Error	0	0.0	0
NA	3042	85.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.dd$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")

10. Reduce worry and concern about cancer
	n	%	val%
Reduce_worry_concern	10	62.5	100
Scantron_Error	0	0.0	0
NA	6	37.5	NA
Total	16	100.0	100

rm(temp.dd)

E10

E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
- E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
- E10_2: 1=Active Surveillance or watchful waiting
- E10_3: 1=Prostate surgery (prostatectomy)
- E10_4: 1=Radiation to the prostate
- E10_5: 1=Hormonal treatments
- E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
- E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
- E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))

All data

  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	201	5.7	100
Scantron_Error	0	0.0	0
NA	3356	94.3	NA
Total	3557	100.0	100

  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	569	16	100
Scantron_Error	0	0	0
NA	2988	84	NA
Total	3557	100	100

  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	1133	31.9	100
Scantron_Error	0	0.0	0
NA	2424	68.1	NA
Total	3557	100.0	100

  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	1396	39.2	100
Scantron_Error	0	0.0	0
NA	2161	60.8	NA
Total	3557	100.0	100

  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	443	12.5	100
Scantron_Error	0	0.0	0
NA	3114	87.5	NA
Total	3557	100.0	100

  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	37	1	100
Scantron_Error	0	0	0
NA	3520	99	NA
Total	3557	100	100

  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	115	3.2	100
Scantron_Error	0	0.0	0
NA	3442	96.8	NA
Total	3557	100.0	100

  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	192	5.4	100
Scantron_Error	0	0.0	0
NA	3365	94.6	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	18	5.6	100
Scantron_Error	0	0.0	0
NA	303	94.4	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	52	16.2	100
Scantron_Error	0	0.0	0
NA	269	83.8	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	150	46.7	100
Scantron_Error	0	0.0	0
NA	171	53.3	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	114	35.5	100
Scantron_Error	0	0.0	0
NA	207	64.5	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	56	17.4	100
Scantron_Error	0	0.0	0
NA	265	82.6	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	2	0.6	100
Scantron_Error	0	0.0	0
NA	319	99.4	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	13	4	100
Scantron_Error	0	0	0
NA	308	96	NA
Total	321	100	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	10	3.1	100
Scantron_Error	0	0.0	0
NA	311	96.9	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	9	4.3	100
Scantron_Error	0	0.0	0
NA	201	95.7	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	46	21.9	100
Scantron_Error	0	0.0	0
NA	164	78.1	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	51	24.3	100
Scantron_Error	0	0.0	0
NA	159	75.7	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	84	40	100
Scantron_Error	0	0	0
NA	126	60	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	28	13.3	100
Scantron_Error	0	0.0	0
NA	182	86.7	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	2	1	100
Scantron_Error	0	0	0
NA	208	99	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	5	2.4	100
Scantron_Error	0	0.0	0
NA	205	97.6	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	12	5.7	100
Scantron_Error	0	0.0	0
NA	198	94.3	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	21	6.7	100
Scantron_Error	0	0.0	0
NA	294	93.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	60	19	100
Scantron_Error	0	0	0
NA	255	81	NA
Total	315	100	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	133	42.2	100
Scantron_Error	0	0.0	0
NA	182	57.8	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	108	34.3	100
Scantron_Error	0	0.0	0
NA	207	65.7	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	52	16.5	100
Scantron_Error	0	0.0	0
NA	263	83.5	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	4	1.3	100
Scantron_Error	0	0.0	0
NA	311	98.7	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	9	2.9	100
Scantron_Error	0	0.0	0
NA	306	97.1	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	10	3.2	100
Scantron_Error	0	0.0	0
NA	305	96.8	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	25	7	100
Scantron_Error	0	0	0
NA	331	93	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	55	15.4	100
Scantron_Error	0	0.0	0
NA	301	84.6	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	124	34.8	100
Scantron_Error	0	0.0	0
NA	232	65.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	135	37.9	100
Scantron_Error	0	0.0	0
NA	221	62.1	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	55	15.4	100
Scantron_Error	0	0.0	0
NA	301	84.6	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	4	1.1	100
Scantron_Error	0	0.0	0
NA	352	98.9	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	15	4.2	100
Scantron_Error	0	0.0	0
NA	341	95.8	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	17	4.8	100
Scantron_Error	0	0.0	0
NA	339	95.2	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	39	6.7	100
Scantron_Error	0	0.0	0
NA	546	93.3	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	74	12.6	100
Scantron_Error	0	0.0	0
NA	511	87.4	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	186	31.8	100
Scantron_Error	0	0.0	0
NA	399	68.2	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	201	34.4	100
Scantron_Error	0	0.0	0
NA	384	65.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	55	9.4	100
Scantron_Error	0	0.0	0
NA	530	90.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	7	1.2	100
Scantron_Error	0	0.0	0
NA	578	98.8	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	21	3.6	100
Scantron_Error	0	0.0	0
NA	564	96.4	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	38	6.5	100
Scantron_Error	0	0.0	0
NA	547	93.5	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	89	5.1	100
Scantron_Error	0	0.0	0
NA	1665	94.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	279	15.9	100
Scantron_Error	0	0.0	0
NA	1475	84.1	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	483	27.5	100
Scantron_Error	0	0.0	0
NA	1271	72.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	746	42.5	100
Scantron_Error	0	0.0	0
NA	1008	57.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	195	11.1	100
Scantron_Error	0	0.0	0
NA	1559	88.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	18	1	100
Scantron_Error	0	0	0
NA	1736	99	NA
Total	1754	100	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	51	2.9	100
Scantron_Error	0	0.0	0
NA	1703	97.1	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	103	5.9	100
Scantron_Error	0	0.0	0
NA	1651	94.1	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")

1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
	n	%	val%
no_treatment	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")

2. Active Surveillance or watchful waiting
	n	%	val%
Active_Surveillance	3	18.8	100
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")

3. Prostate surgery (prostatectomy)
	n	%	val%
prostatectomy	6	37.5	100
Scantron_Error	0	0.0	0
NA	10	62.5	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")

4. Radiation to the prostate
	n	%	val%
Radiation	8	50	100
Scantron_Error	0	0	0
NA	8	50	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")

5. Hormonal treatments
	n	%	val%
Hormonal_treatments	2	12.5	100
Scantron_Error	0	0.0	0
NA	14	87.5	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")

6. Provenge/immunotherapy (Sipuleucel T)
	n	%	val%
Provenge_immunotherapy	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")

7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
	n	%	val%
Chemotherapy	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")

8. Other treatments to the prostate
	n	%	val%
Other	2	12.5	100
Scantron_Error	0	0.0	0
NA	14	87.5	NA
Total	16	100.0	100

rm(temp.dd)

E10-3 Prostatectomy

E10_3. Prostate surgery (prostatectomy), indicate which type(s):
- E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
- E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
- E10_3_3: 1=Had surgery but unsure of type

All data

  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	1174	33	100
Scantron_Error	0	0	0
NA	2383	67	NA
Total	3557	100	100

  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	194	5.5	100
Scantron_Error	0	0.0	0
NA	3363	94.5	NA
Total	3557	100.0	100

  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	226	6.4	100
Scantron_Error	0	0.0	0
NA	3331	93.6	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	165	51.4	100
Scantron_Error	0	0.0	0
NA	156	48.6	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	7	2.2	100
Scantron_Error	0	0.0	0
NA	314	97.8	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	8	2.5	100
Scantron_Error	0	0.0	0
NA	313	97.5	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	46	21.9	100
Scantron_Error	0	0.0	0
NA	164	78.1	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	9	4.3	100
Scantron_Error	0	0.0	0
NA	201	95.7	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	10	4.8	100
Scantron_Error	0	0.0	0
NA	200	95.2	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	151	47.9	100
Scantron_Error	0	0.0	0
NA	164	52.1	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	11	3.5	100
Scantron_Error	0	0.0	0
NA	304	96.5	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	13	4.1	100
Scantron_Error	0	0.0	0
NA	302	95.9	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	121	34	100
Scantron_Error	0	0	0
NA	235	66	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	32	9	100
Scantron_Error	0	0	0
NA	324	91	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	15	4.2	100
Scantron_Error	0	0.0	0
NA	341	95.8	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	206	35.2	100
Scantron_Error	0	0.0	0
NA	379	64.8	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	47	8	100
Scantron_Error	0	0	0
NA	538	92	NA
Total	585	100	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	53	9.1	100
Scantron_Error	0	0.0	0
NA	532	90.9	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	476	27.1	100
Scantron_Error	0	0.0	0
NA	1278	72.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	87	5	100
Scantron_Error	0	0	0
NA	1667	95	NA
Total	1754	100	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	127	7.2	100
Scantron_Error	0	0.0	0
NA	1627	92.8	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")

1. Robotic or laproscopic surgery resulting in removal of the prostate
	n	%	val%
Robotic_laproscopic_surgery	9	56.2	100
Scantron_Error	0	0.0	0
NA	7	43.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")

2. Open surgical removal of the prostate (using a long incision)
	n	%	val%
Open_surgical_removal	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")

3. Had surgery but unsure of type
	n	%	val%
unsure_of_type	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

rm(temp.dd)

E10-4 Radiation

E10_4. Radiation to the prostate, indicate which type(s):
- E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
- E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
- E10_4_3: 1=Other types of radiation therapy, or unsure of what type

All data

  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	1268	35.6	100
Scantron_Error	0	0.0	0
NA	2289	64.4	NA
Total	3557	100.0	100

  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	633	17.8	100
Scantron_Error	0	0.0	0
NA	2924	82.2	NA
Total	3557	100.0	100

  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	214	6	100
Scantron_Error	0	0	0
NA	3343	94	NA
Total	3557	100	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	115	35.8	100
Scantron_Error	0	0.0	0
NA	206	64.2	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	24	7.5	100
Scantron_Error	0	0.0	0
NA	297	92.5	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	13	4	100
Scantron_Error	0	0	0
NA	308	96	NA
Total	321	100	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	78	37.1	100
Scantron_Error	0	0.0	0
NA	132	62.9	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	47	22.4	100
Scantron_Error	0	0.0	0
NA	163	77.6	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	12	5.7	100
Scantron_Error	0	0.0	0
NA	198	94.3	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	95	30.2	100
Scantron_Error	0	0.0	0
NA	220	69.8	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	34	10.8	100
Scantron_Error	0	0.0	0
NA	281	89.2	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	13	4.1	100
Scantron_Error	0	0.0	0
NA	302	95.9	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	124	34.8	100
Scantron_Error	0	0.0	0
NA	232	65.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	28	7.9	100
Scantron_Error	0	0.0	0
NA	328	92.1	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	24	6.7	100
Scantron_Error	0	0.0	0
NA	332	93.3	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	209	35.7	100
Scantron_Error	0	0.0	0
NA	376	64.3	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	50	8.5	100
Scantron_Error	0	0.0	0
NA	535	91.5	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	43	7.4	100
Scantron_Error	0	0.0	0
NA	542	92.6	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	640	36.5	100
Scantron_Error	0	0.0	0
NA	1114	63.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	448	25.5	100
Scantron_Error	0	0.0	0
NA	1306	74.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	108	6.2	100
Scantron_Error	0	0.0	0
NA	1646	93.8	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")

1. External beam radiation
	n	%	val%
External_beam_radiation	7	43.8	100
Scantron_Error	0	0.0	0
NA	9	56.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")

2. brachytherapy
	n	%	val%
brachytherapy	2	12.5	100
Scantron_Error	0	0.0	0
NA	14	87.5	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")

3. Other types
	n	%	val%
Other_types	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

rm(temp.dd)

E10-5 Hormonal treatments

E10_5. Hormonal treatments, indicate which type(s):
- E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
- E10_5_2: 1= Surgical removal of testicles (orchiectomy)
- E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
- E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
- E10_5_5: 1=Had hormone treatment, but unsure of type

All data

  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	622	17.5	100
Scantron_Error	0	0.0	0
NA	2935	82.5	NA
Total	3557	100.0	100

  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	46	1.3	100
Scantron_Error	0	0.0	0
NA	3511	98.7	NA
Total	3557	100.0	100

  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	78	2.2	100
Scantron_Error	0	0.0	0
NA	3479	97.8	NA
Total	3557	100.0	100

  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	56	1.6	100
Scantron_Error	0	0.0	0
NA	3501	98.4	NA
Total	3557	100.0	100

  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	182	5.1	100
Scantron_Error	0	0.0	0
NA	3375	94.9	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	72	22.4	100
Scantron_Error	0	0.0	0
NA	249	77.6	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	1	0.3	100
Scantron_Error	0	0.0	0
NA	320	99.7	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	11	3.4	100
Scantron_Error	0	0.0	0
NA	310	96.6	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	5	1.6	100
Scantron_Error	0	0.0	0
NA	316	98.4	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	10	3.1	100
Scantron_Error	0	0.0	0
NA	311	96.9	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	53	25.2	100
Scantron_Error	0	0.0	0
NA	157	74.8	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	0	0	NaN
Scantron_Error	0	0	NaN
NA	210	100	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	3	1.4	100
Scantron_Error	0	0.0	0
NA	207	98.6	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	2	1	100
Scantron_Error	0	0	0
NA	208	99	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	5	2.4	100
Scantron_Error	0	0.0	0
NA	205	97.6	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	62	19.7	100
Scantron_Error	0	0.0	0
NA	253	80.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	3	1	100
Scantron_Error	0	0	0
NA	312	99	NA
Total	315	100	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	14	4.4	100
Scantron_Error	0	0.0	0
NA	301	95.6	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	8	2.5	100
Scantron_Error	0	0.0	0
NA	307	97.5	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	12	3.8	100
Scantron_Error	0	0.0	0
NA	303	96.2	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	64	18	100
Scantron_Error	0	0	0
NA	292	82	NA
Total	356	100	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	6	1.7	100
Scantron_Error	0	0.0	0
NA	350	98.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	6	1.7	100
Scantron_Error	0	0.0	0
NA	350	98.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	5	1.4	100
Scantron_Error	0	0.0	0
NA	351	98.6	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	21	5.9	100
Scantron_Error	0	0.0	0
NA	335	94.1	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	99	16.9	100
Scantron_Error	0	0.0	0
NA	486	83.1	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	11	1.9	100
Scantron_Error	0	0.0	0
NA	574	98.1	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	8	1.4	100
Scantron_Error	0	0.0	0
NA	577	98.6	NA
Total	585	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	6	1	100
Scantron_Error	0	0	0
NA	579	99	NA
Total	585	100	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	44	7.5	100
Scantron_Error	0	0.0	0
NA	541	92.5	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	269	15.3	100
Scantron_Error	0	0.0	0
NA	1485	84.7	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	25	1.4	100
Scantron_Error	0	0.0	0
NA	1729	98.6	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	36	2.1	100
Scantron_Error	0	0.0	0
NA	1718	97.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	30	1.7	100
Scantron_Error	0	0.0	0
NA	1724	98.3	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	90	5.1	100
Scantron_Error	0	0.0	0
NA	1664	94.9	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")

1. Hormone shots
	n	%	val%
Hormone_shots	3	18.8	100
Scantron_Error	0	0.0	0
NA	13	81.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")

2. orchiectomy
	n	%	val%
orchiectomy	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")

3. Casodex or Eulexin pills
	n	%	val%
Casodex_Eulexin	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")

4. Zytiga or Xtandi pills
	n	%	val%
Zytiga_Xtandi	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")

5. unsure of type
	n	%	val%
unsure_type	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

rm(temp.dd)

E11

E11. Your treatment decision: How true is each of the following statements for you?
- 1. I had all the information I needed when a treatment was chosen for my prostate cancer
- 1. My doctors told me the whole story about the effects of treatment
- 1. I knew the right questions to ask my doctor
- 1. I had enough time to make a decision about my treatment
- 1. I am satisfied with the choices I made in treating my prostate cancer
- 1. I would recommend the treatment I had to a close relative or friend
  - 1=Not at all
  - 2=A little bit
  - 3=Somewhat
  - 4=Quite a bit
  - 5=Very much

All data

  e11a <- as.factor(d[,"e11a"])
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	111	3.1	3.3
A_little_bit	126	3.5	3.7
Somewhat	415	11.7	12.2
Quite_a_bit	874	24.6	25.7
Very_much	1865	52.4	54.9
Scantron_Error	6	0.2	0.2
NA	160	4.5	NA
Total	3557	100.0	100.0

  e11b <- as.factor(d[,"e11b"])
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	82	2.3	2.4
A_little_bit	140	3.9	4.1
Somewhat	407	11.4	11.9
Quite_a_bit	834	23.4	24.5
Very_much	1940	54.5	56.9
Scantron_Error	4	0.1	0.1
NA	150	4.2	NA
Total	3557	100.0	100.0

  e11c <- as.factor(d[,"e11c"])
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	336	9.4	9.9
A_little_bit	405	11.4	11.9
Somewhat	1123	31.6	33.0
Quite_a_bit	668	18.8	19.6
Very_much	856	24.1	25.2
Scantron_Error	12	0.3	0.4
NA	157	4.4	NA
Total	3557	100.0	100.0

  e11d <- as.factor(d[,"e11d"])
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	87	2.4	2.6
A_little_bit	141	4.0	4.1
Somewhat	529	14.9	15.6
Quite_a_bit	847	23.8	24.9
Very_much	1787	50.2	52.6
Scantron_Error	7	0.2	0.2
NA	159	4.5	NA
Total	3557	100.0	100.0

  e11e <- as.factor(d[,"e11e"])
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	137	3.9	4.0
A_little_bit	125	3.5	3.7
Somewhat	413	11.6	12.1
Quite_a_bit	518	14.6	15.2
Very_much	2205	62.0	64.8
Scantron_Error	5	0.1	0.1
NA	154	4.3	NA
Total	3557	100.0	100.0

  e11f <- as.factor(d[,"e11f"])
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5",
                       Scantron_Error="*")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	191	5.4	5.7
A_little_bit	133	3.7	3.9
Somewhat	495	13.9	14.7
Quite_a_bit	461	13.0	13.7
Very_much	2092	58.8	62.0
Scantron_Error	4	0.1	0.1
NA	181	5.1	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	9	2.8	2.8
A_little_bit	19	5.9	6.0
Somewhat	45	14.0	14.2
Quite_a_bit	102	31.8	32.2
Very_much	142	44.2	44.8
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	6	1.9	1.9
A_little_bit	18	5.6	5.7
Somewhat	44	13.7	13.9
Quite_a_bit	97	30.2	30.6
Very_much	152	47.4	47.9
Scantron_Error	0	0.0	0.0
NA	4	1.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	33	10.3	10.4
A_little_bit	55	17.1	17.3
Somewhat	99	30.8	31.1
Quite_a_bit	73	22.7	23.0
Very_much	58	18.1	18.2
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	11	3.4	3.5
A_little_bit	9	2.8	2.8
Somewhat	53	16.5	16.7
Quite_a_bit	98	30.5	30.8
Very_much	147	45.8	46.2
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	13	4.0	4.1
A_little_bit	8	2.5	2.5
Somewhat	39	12.1	12.3
Quite_a_bit	66	20.6	20.8
Very_much	192	59.8	60.4
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	13	4.0	4.1
A_little_bit	18	5.6	5.7
Somewhat	63	19.6	19.8
Quite_a_bit	64	19.9	20.1
Very_much	160	49.8	50.3
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	5	2.4	2.5
A_little_bit	2	1.0	1.0
Somewhat	24	11.4	11.8
Quite_a_bit	52	24.8	25.5
Very_much	121	57.6	59.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	5	2.4	2.5
A_little_bit	4	1.9	2.0
Somewhat	23	11.0	11.3
Quite_a_bit	47	22.4	23.0
Very_much	125	59.5	61.3
Scantron_Error	0	0.0	0.0
NA	6	2.9	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	15	7.1	7.4
A_little_bit	22	10.5	10.8
Somewhat	74	35.2	36.5
Quite_a_bit	40	19.0	19.7
Very_much	52	24.8	25.6
Scantron_Error	0	0.0	0.0
NA	7	3.3	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	2	1.0	1.0
A_little_bit	7	3.3	3.5
Somewhat	32	15.2	15.8
Quite_a_bit	44	21.0	21.8
Very_much	117	55.7	57.9
Scantron_Error	0	0.0	0.0
NA	8	3.8	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	7	3.3	3.4
A_little_bit	10	4.8	4.9
Somewhat	28	13.3	13.7
Quite_a_bit	23	11.0	11.2
Very_much	137	65.2	66.8
Scantron_Error	0	0.0	0.0
NA	5	2.4	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	9	4.3	4.4
A_little_bit	6	2.9	3.0
Somewhat	33	15.7	16.3
Quite_a_bit	23	11.0	11.3
Very_much	131	62.4	64.5
Scantron_Error	1	0.5	0.5
NA	7	3.3	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	11	3.5	3.7
A_little_bit	8	2.5	2.7
Somewhat	36	11.4	12.0
Quite_a_bit	71	22.5	23.7
Very_much	174	55.2	58.0
Scantron_Error	0	0.0	0.0
NA	15	4.8	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	7	2.2	2.3
A_little_bit	11	3.5	3.6
Somewhat	41	13.0	13.5
Quite_a_bit	78	24.8	25.7
Very_much	166	52.7	54.8
Scantron_Error	0	0.0	0.0
NA	12	3.8	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	31	9.8	10.3
A_little_bit	36	11.4	12.0
Somewhat	97	30.8	32.2
Quite_a_bit	56	17.8	18.6
Very_much	81	25.7	26.9
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	10	3.2	3.3
A_little_bit	20	6.3	6.6
Somewhat	49	15.6	16.3
Quite_a_bit	86	27.3	28.6
Very_much	136	43.2	45.2
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	14	4.4	4.7
A_little_bit	9	2.9	3.0
Somewhat	44	14.0	14.6
Quite_a_bit	49	15.6	16.3
Very_much	185	58.7	61.5
Scantron_Error	0	0.0	0.0
NA	14	4.4	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	23	7.3	7.8
A_little_bit	15	4.8	5.1
Somewhat	41	13.0	13.9
Quite_a_bit	47	14.9	16.0
Very_much	168	53.3	57.1
Scantron_Error	0	0.0	0.0
NA	21	6.7	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	12	3.4	3.5
A_little_bit	14	3.9	4.1
Somewhat	48	13.5	14.2
Quite_a_bit	83	23.3	24.5
Very_much	182	51.1	53.7
Scantron_Error	0	0.0	0.0
NA	17	4.8	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	10	2.8	2.9
A_little_bit	15	4.2	4.4
Somewhat	52	14.6	15.3
Quite_a_bit	76	21.3	22.4
Very_much	186	52.2	54.9
Scantron_Error	0	0.0	0.0
NA	17	4.8	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	37	10.4	10.9
A_little_bit	50	14.0	14.7
Somewhat	106	29.8	31.2
Quite_a_bit	59	16.6	17.4
Very_much	86	24.2	25.3
Scantron_Error	2	0.6	0.6
NA	16	4.5	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	9	2.5	2.7
A_little_bit	16	4.5	4.7
Somewhat	56	15.7	16.6
Quite_a_bit	89	25.0	26.3
Very_much	167	46.9	49.4
Scantron_Error	1	0.3	0.3
NA	18	5.1	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	11	3.1	3.2
A_little_bit	15	4.2	4.4
Somewhat	47	13.2	13.9
Quite_a_bit	61	17.1	18.0
Very_much	205	57.6	60.5
Scantron_Error	0	0.0	0.0
NA	17	4.8	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	18	5.1	5.4
A_little_bit	16	4.5	4.8
Somewhat	59	16.6	17.6
Quite_a_bit	50	14.0	14.9
Very_much	192	53.9	57.3
Scantron_Error	0	0.0	0.0
NA	21	5.9	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	20	3.4	3.6
A_little_bit	30	5.1	5.4
Somewhat	66	11.3	11.9
Quite_a_bit	144	24.6	25.9
Very_much	295	50.4	53.1
Scantron_Error	1	0.2	0.2
NA	29	5.0	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	13	2.2	2.3
A_little_bit	34	5.8	6.1
Somewhat	54	9.2	9.7
Quite_a_bit	126	21.5	22.6
Very_much	330	56.4	59.2
Scantron_Error	0	0.0	0.0
NA	28	4.8	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	68	11.6	12.2
A_little_bit	60	10.3	10.8
Somewhat	168	28.7	30.2
Quite_a_bit	103	17.6	18.5
Very_much	156	26.7	28.1
Scantron_Error	1	0.2	0.2
NA	29	5.0	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	15	2.6	2.7
A_little_bit	19	3.2	3.4
Somewhat	91	15.6	16.4
Quite_a_bit	128	21.9	23.1
Very_much	301	51.5	54.2
Scantron_Error	1	0.2	0.2
NA	30	5.1	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	19	3.2	3.4
A_little_bit	20	3.4	3.6
Somewhat	62	10.6	11.2
Quite_a_bit	84	14.4	15.1
Very_much	370	63.2	66.5
Scantron_Error	1	0.2	0.2
NA	29	5.0	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	28	4.8	5.1
A_little_bit	20	3.4	3.6
Somewhat	75	12.8	13.6
Quite_a_bit	58	9.9	10.5
Very_much	370	63.2	67.2
Scantron_Error	0	0.0	0.0
NA	34	5.8	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	52	3.0	3.1
A_little_bit	53	3.0	3.2
Somewhat	195	11.1	11.7
Quite_a_bit	415	23.7	24.9
Very_much	945	53.9	56.8
Scantron_Error	5	0.3	0.3
NA	89	5.1	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	40	2.3	2.4
A_little_bit	58	3.3	3.5
Somewhat	190	10.8	11.4
Quite_a_bit	406	23.1	24.3
Very_much	973	55.5	58.2
Scantron_Error	4	0.2	0.2
NA	83	4.7	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	149	8.5	8.9
A_little_bit	180	10.3	10.8
Somewhat	574	32.7	34.5
Quite_a_bit	334	19.0	20.0
Very_much	420	23.9	25.2
Scantron_Error	9	0.5	0.5
NA	88	5.0	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	38	2.2	2.3
A_little_bit	69	3.9	4.1
Somewhat	246	14.0	14.7
Quite_a_bit	399	22.7	23.9
Very_much	912	52.0	54.6
Scantron_Error	5	0.3	0.3
NA	85	4.8	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	72	4.1	4.3
A_little_bit	63	3.6	3.8
Somewhat	192	10.9	11.5
Quite_a_bit	230	13.1	13.8
Very_much	1107	63.1	66.4
Scantron_Error	4	0.2	0.2
NA	86	4.9	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	99	5.6	6.0
A_little_bit	57	3.2	3.4
Somewhat	224	12.8	13.5
Quite_a_bit	212	12.1	12.8
Very_much	1064	60.7	64.1
Scantron_Error	3	0.2	0.2
NA	95	5.4	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")

a. I had all the information I needed when a treatment was chosen for my prostate cancer
	n	%	val%
Not_at_all	2	12.5	12.5
A_little_bit	0	0.0	0.0
Somewhat	1	6.2	6.2
Quite_a_bit	7	43.8	43.8
Very_much	6	37.5	37.5
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")

b. My doctors told me the whole story about the effects of treatment
	n	%	val%
Not_at_all	1	6.2	6.2
A_little_bit	0	0.0	0.0
Somewhat	3	18.8	18.8
Quite_a_bit	4	25.0	25.0
Very_much	8	50.0	50.0
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")

c. I knew the right questions to ask my doctor
	n	%	val%
Not_at_all	3	18.8	18.8
A_little_bit	2	12.5	12.5
Somewhat	5	31.2	31.2
Quite_a_bit	3	18.8	18.8
Very_much	3	18.8	18.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")

d. I had enough time to make a decision about my treatment
	n	%	val%
Not_at_all	2	12.5	13.3
A_little_bit	1	6.2	6.7
Somewhat	2	12.5	13.3
Quite_a_bit	3	18.8	20.0
Very_much	7	43.8	46.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")

e. I am satisfied with the choices I made in treating my prostate cancer
	n	%	val%
Not_at_all	1	6.2	6.2
A_little_bit	0	0.0	0.0
Somewhat	1	6.2	6.2
Quite_a_bit	5	31.2	31.2
Very_much	9	56.2	56.2
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")

f. I would recommend the treatment I had to a close relative or friend
	n	%	val%
Not_at_all	1	6.2	6.2
A_little_bit	1	6.2	6.2
Somewhat	0	0.0	0.0
Quite_a_bit	7	43.8	43.8
Very_much	7	43.8	43.8
Scantron_Error	0	0.0	0.0
Total	16	100.0	100.0

rm(temp.dd)

E12

E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure

All data

  e12 <- as.factor(d[,"e12"])
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e12 <- ordered(e12, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	940	26.4	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	2617	73.6	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	74	23.1	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	247	76.9	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	45	21.4	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	165	78.6	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	84	26.7	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	231	73.3	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	112	31.5	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	244	68.5	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	157	26.8	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	428	73.2	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	463	26.4	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	1291	73.6	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")

e12
	n	%	val%
No	5	31.2	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	11	68.8	NA
Total	16	100.0	100

rm(temp.dd)

E13

E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
- 0=None
- 1=1
- 2=2
- 3=3
- 4=4 or more
- 88=Don’t know/not sure

All data

  e13 <- as.factor(d[,"e13"])
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88",
                     Scantron_Error="*")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	658	18.5	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	2899	81.5	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	75	23.4	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	246	76.6	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0	0
One	40	19	100
Two	0	0	0
Three	0	0	0
Four_more	0	0	0
Dont_know	0	0	0
Scantron_Error	0	0	0
NA	170	81	NA
Total	210	100	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	57	18.1	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	258	81.9	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	59	16.6	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	297	83.4	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	112	19.1	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	473	80.9	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0.0	0
One	311	17.7	100
Two	0	0.0	0
Three	0	0.0	0
Four_more	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	1443	82.3	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")

e13
	n	%	val%
None	0	0	0
One	4	25	100
Two	0	0	0
Three	0	0	0
Four_more	0	0	0
Dont_know	0	0	0
Scantron_Error	0	0	0
NA	12	75	NA
Total	16	100	100

rm(temp.dd)

E14

E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure

All data

  e14 <- as.factor(d[,"e14"])
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e14 <- ordered(e14, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	68	1.9	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	3489	98.1	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	5	1.6	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	316	98.4	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	2	1	100
Yes	0	0	0
Dont_know	0	0	0
Scantron_Error	0	0	0
NA	208	99	NA
Total	210	100	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	7	2.2	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	308	97.8	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	7	2	100
Yes	0	0	0
Dont_know	0	0	0
Scantron_Error	0	0	0
NA	349	98	NA
Total	356	100	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	14	2.4	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	571	97.6	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	31	1.8	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	1723	98.2	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")

e14
	n	%	val%
No	2	12.5	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	14	87.5	NA
Total	16	100.0	100

rm(temp.dd)

E15

E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure

All data

  e15 <- as.factor(d[,"e15"])
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88",
                     Scantron_Error="*")
  e15 <- ordered(e15, c("No","Yes","Dont_know","Scantron_Error"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	147	4.1	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	3410	95.9	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	5	1.6	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	316	98.4	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	6	2.9	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	204	97.1	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	16	5.1	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	299	94.9	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	27	7.6	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	329	92.4	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	13	2.2	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	572	97.8	NA
Total	585	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	79	4.5	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	1675	95.5	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")

e15
	n	%	val%
No	1	6.2	100
Yes	0	0.0	0
Dont_know	0	0.0	0
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

rm(temp.dd)

F1

F1. How tall are you?

All data

  f1cm <- d[,"f1cm"]
 
  
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")

How tall are you? (cm)
	n	%	val%
0	6	0.2	0.2
0 9	1	0.0	0.0
1	9	0.3	0.3
10	4	0.1	0.1
11	4	0.1	0.1
111	1	0.0	0.0
12	1	0.0	0.0
135	1	0.0	0.0
148	1	0.0	0.0
152	1	0.0	0.0
164	1	0.0	0.0
165	1	0.0	0.0
170	1	0.0	0.0
173	1	0.0	0.0
175	1	0.0	0.0
178	2	0.1	0.1
180	1	0.0	0.0
181	1	0.0	0.0
185	2	0.1	0.1
190	3	0.1	0.1
2	5	0.1	0.1
225	1	0.0	0.0
228	1	0.0	0.0
234	1	0.0	0.0
245	1	0.0	0.0
247	1	0.0	0.0
255	1	0.0	0.0
265	1	0.0	0.0
280	1	0.0	0.0
290	1	0.0	0.0
3	1	0.0	0.0
44	1	0.0	0.0
47	1	0.0	0.0
5	4	0.1	0.1
6	2	0.1	0.1
7	3	0.1	0.1
72	1	0.0	0.0
78	1	0.0	0.0
8	2	0.1	0.1
9	5	0.1	0.1
“NA”	3479	97.8	97.8
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
1	1	0.3	0.3
3	1	0.3	0.3
“NA”	319	99.4	99.4
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
185	1	0.5	0.5
8	1	0.5	0.5
“NA”	208	99.0	99.0
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
11	1	0.3	0.3
178	1	0.3	0.3
“NA”	313	99.4	99.4
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
111	1	0.3	0.3
148	1	0.3	0.3
2	1	0.3	0.3
225	1	0.3	0.3
5	1	0.3	0.3
7	1	0.3	0.3
9	1	0.3	0.3
“NA”	349	98.0	98.0
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
0	1	0.2	0.2
1	2	0.3	0.3
10	1	0.2	0.2
152	1	0.2	0.2
164	1	0.2	0.2
165	1	0.2	0.2
185	1	0.2	0.2
2	1	0.2	0.2
255	1	0.2	0.2
290	1	0.2	0.2
5	1	0.2	0.2
78	1	0.2	0.2
“NA”	572	97.8	97.8
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
0	5	0.3	0.3
0 9	1	0.1	0.1
1	6	0.3	0.3
10	3	0.2	0.2
11	3	0.2	0.2
12	1	0.1	0.1
135	1	0.1	0.1
170	1	0.1	0.1
173	1	0.1	0.1
175	1	0.1	0.1
178	1	0.1	0.1
180	1	0.1	0.1
181	1	0.1	0.1
190	3	0.2	0.2
2	3	0.2	0.2
228	1	0.1	0.1
234	1	0.1	0.1
245	1	0.1	0.1
247	1	0.1	0.1
265	1	0.1	0.1
280	1	0.1	0.1
44	1	0.1	0.1
47	1	0.1	0.1
5	2	0.1	0.1
6	2	0.1	0.1
7	2	0.1	0.1
72	1	0.1	0.1
8	1	0.1	0.1
9	4	0.2	0.2
“NA”	1702	97.0	97.0
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f1cm")

f1cm
	n	%	val%
“NA”	16	100	100
Total	16	100	100

rm(temp.dd)

F2

F2. How much do you current weight?

All data

  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")

How much do you current weight? (lbs)
	n	%	val%
*	2	0.1	0.1
* 3	1	0.0	0.0
* 4	1	0.0	0.0
* 5	1	0.0	0.0
* 9	1	0.0	0.0
*0	2	0.1	0.1
*2	1	0.0	0.0
*35	1	0.0	0.0
*4	2	0.1	0.1
*5	2	0.1	0.1
*99	1	0.0	0.0
0	2	0.1	0.1
0*	1	0.0	0.0
1	8	0.2	0.2
1 *	3	0.1	0.1
1 8	1	0.0	0.0
1*	3	0.1	0.1
106	1	0.0	0.0
110	2	0.1	0.1
111	1	0.0	0.0
112	1	0.0	0.0
114	1	0.0	0.0
115	3	0.1	0.1
117	1	0.0	0.0
119	1	0.0	0.0
12	1	0.0	0.0
120	6	0.2	0.2
121	1	0.0	0.0
122	1	0.0	0.0
124	1	0.0	0.0
125	5	0.1	0.1
126	1	0.0	0.0
127	3	0.1	0.1
130	14	0.4	0.4
132	1	0.0	0.0
133	1	0.0	0.0
134	1	0.0	0.0
135	9	0.3	0.3
137	7	0.2	0.2
138	6	0.2	0.2
139	4	0.1	0.1
14*	1	0.0	0.0
140	20	0.6	0.6
141	3	0.1	0.1
142	5	0.1	0.1
143	2	0.1	0.1
144	1	0.0	0.0
145	15	0.4	0.4
146	4	0.1	0.1
147	8	0.2	0.2
148	8	0.2	0.2
149	2	0.1	0.1
150	40	1.1	1.1
151	1	0.0	0.0
152	8	0.2	0.2
153	7	0.2	0.2
154	10	0.3	0.3
155	35	1.0	1.0
156	11	0.3	0.3
157	8	0.2	0.2
158	15	0.4	0.4
159	7	0.2	0.2
160	57	1.6	1.6
161	7	0.2	0.2
162	16	0.4	0.4
163	12	0.3	0.3
164	10	0.3	0.3
165	50	1.4	1.4
166	6	0.2	0.2
167	21	0.6	0.6
168	24	0.7	0.7
169	12	0.3	0.3
170	71	2.0	2.0
171	9	0.3	0.3
172	18	0.5	0.5
173	18	0.5	0.5
174	15	0.4	0.4
175	68	1.9	1.9
176	17	0.5	0.5
177	13	0.4	0.4
178	31	0.9	0.9
179	8	0.2	0.2
18	2	0.1	0.1
180	84	2.4	2.4
181	7	0.2	0.2
182	26	0.7	0.7
183	20	0.6	0.6
184	18	0.5	0.5
185	85	2.4	2.4
186	16	0.4	0.4
187	23	0.6	0.6
188	31	0.9	0.9
189	28	0.8	0.8
190	98	2.8	2.8
191	9	0.3	0.3
192	24	0.7	0.7
193	21	0.6	0.6
194	12	0.3	0.3
195	66	1.9	1.9
196	17	0.5	0.5
197	25	0.7	0.7
198	38	1.1	1.1
199	18	0.5	0.5
2	10	0.3	0.3
2 *	1	0.0	0.0
2 1	1	0.0	0.0
2 2	1	0.0	0.0
2 6	1	0.0	0.0
2*	5	0.1	0.1
200	94	2.6	2.6
201	3	0.1	0.1
202	19	0.5	0.5
203	12	0.3	0.3
204	21	0.6	0.6
205	77	2.2	2.2
206	14	0.4	0.4
207	22	0.6	0.6
208	22	0.6	0.6
209	15	0.4	0.4
210	99	2.8	2.8
211	8	0.2	0.2
212	32	0.9	0.9
213	5	0.1	0.1
214	28	0.8	0.8
215	97	2.7	2.7
216	6	0.2	0.2
217	9	0.3	0.3
218	28	0.8	0.8
219	8	0.2	0.2
220	89	2.5	2.5
221	10	0.3	0.3
222	16	0.4	0.4
223	16	0.4	0.4
224	15	0.4	0.4
225	52	1.5	1.5
226	7	0.2	0.2
227	7	0.2	0.2
228	11	0.3	0.3
229	10	0.3	0.3
23	1	0.0	0.0
230	72	2.0	2.0
231	2	0.1	0.1
232	12	0.3	0.3
233	12	0.3	0.3
234	14	0.4	0.4
235	41	1.2	1.2
236	6	0.2	0.2
237	12	0.3	0.3
238	8	0.2	0.2
239	3	0.1	0.1
240	58	1.6	1.6
241	2	0.1	0.1
242	17	0.5	0.5
243	8	0.2	0.2
244	5	0.1	0.1
245	40	1.1	1.1
246	10	0.3	0.3
247	13	0.4	0.4
248	6	0.2	0.2
249	8	0.2	0.2
250	54	1.5	1.5
251	5	0.1	0.1
252	9	0.3	0.3
253	3	0.1	0.1
254	12	0.3	0.3
255	16	0.4	0.4
256	2	0.1	0.1
257	4	0.1	0.1
258	4	0.1	0.1
259	4	0.1	0.1
260	42	1.2	1.2
261	3	0.1	0.1
262	12	0.3	0.3
263	5	0.1	0.1
264	5	0.1	0.1
265	26	0.7	0.7
266	5	0.1	0.1
267	5	0.1	0.1
268	3	0.1	0.1
269	1	0.0	0.0
270	28	0.8	0.8
271	3	0.1	0.1
272	4	0.1	0.1
273	2	0.1	0.1
274	3	0.1	0.1
275	13	0.4	0.4
276	4	0.1	0.1
277	4	0.1	0.1
278	3	0.1	0.1
279	2	0.1	0.1
280	19	0.5	0.5
281	1	0.0	0.0
282	1	0.0	0.0
284	2	0.1	0.1
285	9	0.3	0.3
286	1	0.0	0.0
287	3	0.1	0.1
288	1	0.0	0.0
289	6	0.2	0.2
29	1	0.0	0.0
290	9	0.3	0.3
292	3	0.1	0.1
294	2	0.1	0.1
295	7	0.2	0.2
296	1	0.0	0.0
297	5	0.1	0.1
298	5	0.1	0.1
3	1	0.0	0.0
300	15	0.4	0.4
302	1	0.0	0.0
303	1	0.0	0.0
305	2	0.1	0.1
307	2	0.1	0.1
308	1	0.0	0.0
309	1	0.0	0.0
310	9	0.3	0.3
311	1	0.0	0.0
314	1	0.0	0.0
315	4	0.1	0.1
316	1	0.0	0.0
317	2	0.1	0.1
319	1	0.0	0.0
320	6	0.2	0.2
321	2	0.1	0.1
324	2	0.1	0.1
325	3	0.1	0.1
326	1	0.0	0.0
330	7	0.2	0.2
334	1	0.0	0.0
335	4	0.1	0.1
340	3	0.1	0.1
344	1	0.0	0.0
350	3	0.1	0.1
355	2	0.1	0.1
358	1	0.0	0.0
360	1	0.0	0.0
361	1	0.0	0.0
362	1	0.0	0.0
365	1	0.0	0.0
370	1	0.0	0.0
375	1	0.0	0.0
376	1	0.0	0.0
397	1	0.0	0.0
400	2	0.1	0.1
410	1	0.0	0.0
415	1	0.0	0.0
416	1	0.0	0.0
424	1	0.0	0.0
430	1	0.0	0.0
440	1	0.0	0.0
50	1	0.0	0.0
53	1	0.0	0.0
60	1	0.0	0.0
65	3	0.1	0.1
68	2	0.1	0.1
7	1	0.0	0.0
71	1	0.0	0.0
72	1	0.0	0.0
74	1	0.0	0.0
75	1	0.0	0.0
76	1	0.0	0.0
78	1	0.0	0.0
80	2	0.1	0.1
81	1	0.0	0.0
84	1	0.0	0.0
89	1	0.0	0.0
90	2	0.1	0.1
92	1	0.0	0.0
97	1	0.0	0.0
98	1	0.0	0.0
“NA”	386	10.9	10.9
Total	3557	100.0	100.0

  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
*	1	0.0	0.0
0	10	0.3	0.3
1	2	0.1	0.1
1 7	1	0.0	0.0
10	1	0.0	0.0
110	1	0.0	0.0
111	2	0.1	0.1
113	1	0.0	0.0
137	1	0.0	0.0
175	1	0.0	0.0
2	4	0.1	0.1
22	1	0.0	0.0
3	1	0.0	0.0
37	1	0.0	0.0
45	1	0.0	0.0
5	1	0.0	0.0
50	3	0.1	0.1
55	2	0.1	0.1
6	1	0.0	0.0
60	1	0.0	0.0
61	1	0.0	0.0
64	1	0.0	0.0
65	1	0.0	0.0
7	1	0.0	0.0
75	1	0.0	0.0
76	1	0.0	0.0
8	1	0.0	0.0
82	1	0.0	0.0
85	1	0.0	0.0
86	1	0.0	0.0
88	1	0.0	0.0
9	2	0.1	0.1
90	1	0.0	0.0
91	1	0.0	0.0
92	1	0.0	0.0
“NA”	3504	98.5	98.5
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*0	1	0.3	0.3
*99	1	0.3	0.3
115	1	0.3	0.3
120	1	0.3	0.3
125	1	0.3	0.3
130	1	0.3	0.3
137	1	0.3	0.3
138	1	0.3	0.3
140	2	0.6	0.6
145	2	0.6	0.6
148	1	0.3	0.3
150	4	1.2	1.2
152	1	0.3	0.3
155	2	0.6	0.6
158	2	0.6	0.6
160	10	3.1	3.1
163	2	0.6	0.6
164	1	0.3	0.3
165	2	0.6	0.6
166	1	0.3	0.3
167	3	0.9	0.9
168	3	0.9	0.9
169	1	0.3	0.3
170	7	2.2	2.2
172	2	0.6	0.6
173	4	1.2	1.2
175	6	1.9	1.9
176	2	0.6	0.6
177	2	0.6	0.6
178	4	1.2	1.2
180	7	2.2	2.2
181	1	0.3	0.3
182	1	0.3	0.3
183	3	0.9	0.9
184	2	0.6	0.6
185	6	1.9	1.9
186	3	0.9	0.9
187	2	0.6	0.6
188	2	0.6	0.6
189	1	0.3	0.3
190	7	2.2	2.2
191	2	0.6	0.6
192	3	0.9	0.9
193	1	0.3	0.3
195	4	1.2	1.2
196	1	0.3	0.3
197	1	0.3	0.3
198	3	0.9	0.9
199	1	0.3	0.3
2*	1	0.3	0.3
200	12	3.7	3.7
202	2	0.6	0.6
203	1	0.3	0.3
204	1	0.3	0.3
205	13	4.0	4.0
206	2	0.6	0.6
207	1	0.3	0.3
208	2	0.6	0.6
209	2	0.6	0.6
210	14	4.4	4.4
212	2	0.6	0.6
214	3	0.9	0.9
215	12	3.7	3.7
216	1	0.3	0.3
217	1	0.3	0.3
218	3	0.9	0.9
220	7	2.2	2.2
221	1	0.3	0.3
222	3	0.9	0.9
223	3	0.9	0.9
224	3	0.9	0.9
225	8	2.5	2.5
230	5	1.6	1.6
233	1	0.3	0.3
235	5	1.6	1.6
236	1	0.3	0.3
239	1	0.3	0.3
240	4	1.2	1.2
242	3	0.9	0.9
244	1	0.3	0.3
245	4	1.2	1.2
247	2	0.6	0.6
248	1	0.3	0.3
249	3	0.9	0.9
250	9	2.8	2.8
252	1	0.3	0.3
254	1	0.3	0.3
255	2	0.6	0.6
260	3	0.9	0.9
262	2	0.6	0.6
263	1	0.3	0.3
264	1	0.3	0.3
265	4	1.2	1.2
270	6	1.9	1.9
271	1	0.3	0.3
274	2	0.6	0.6
275	2	0.6	0.6
276	1	0.3	0.3
280	1	0.3	0.3
285	2	0.6	0.6
289	1	0.3	0.3
298	1	0.3	0.3
300	3	0.9	0.9
310	3	0.9	0.9
321	1	0.3	0.3
324	1	0.3	0.3
355	1	0.3	0.3
68	1	0.3	0.3
“NA”	18	5.6	5.6
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
*	1	0.3	0.3
137	1	0.3	0.3
“NA”	319	99.4	99.4
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*	1	0.5	0.5
1	1	0.5	0.5
127	2	1.0	1.0
140	2	1.0	1.0
145	1	0.5	0.5
150	4	1.9	1.9
154	1	0.5	0.5
160	2	1.0	1.0
161	1	0.5	0.5
164	1	0.5	0.5
165	3	1.4	1.4
167	1	0.5	0.5
168	3	1.4	1.4
169	2	1.0	1.0
170	3	1.4	1.4
171	1	0.5	0.5
172	1	0.5	0.5
173	1	0.5	0.5
174	1	0.5	0.5
175	4	1.9	1.9
177	1	0.5	0.5
178	1	0.5	0.5
180	6	2.9	2.9
182	1	0.5	0.5
184	2	1.0	1.0
185	1	0.5	0.5
186	1	0.5	0.5
187	2	1.0	1.0
188	2	1.0	1.0
189	2	1.0	1.0
190	9	4.3	4.3
192	2	1.0	1.0
193	1	0.5	0.5
194	1	0.5	0.5
195	10	4.8	4.8
196	1	0.5	0.5
197	3	1.4	1.4
198	2	1.0	1.0
199	1	0.5	0.5
2*	2	1.0	1.0
200	5	2.4	2.4
204	1	0.5	0.5
205	4	1.9	1.9
207	1	0.5	0.5
210	4	1.9	1.9
211	1	0.5	0.5
212	3	1.4	1.4
214	2	1.0	1.0
215	1	0.5	0.5
216	1	0.5	0.5
220	11	5.2	5.2
221	1	0.5	0.5
224	3	1.4	1.4
225	3	1.4	1.4
226	1	0.5	0.5
228	1	0.5	0.5
229	3	1.4	1.4
23	1	0.5	0.5
230	5	2.4	2.4
233	1	0.5	0.5
235	2	1.0	1.0
237	2	1.0	1.0
240	3	1.4	1.4
243	2	1.0	1.0
245	1	0.5	0.5
247	1	0.5	0.5
249	2	1.0	1.0
250	1	0.5	0.5
254	1	0.5	0.5
255	3	1.4	1.4
260	3	1.4	1.4
262	1	0.5	0.5
265	1	0.5	0.5
266	1	0.5	0.5
267	1	0.5	0.5
268	1	0.5	0.5
270	4	1.9	1.9
272	1	0.5	0.5
280	1	0.5	0.5
285	1	0.5	0.5
292	1	0.5	0.5
310	1	0.5	0.5
311	1	0.5	0.5
315	1	0.5	0.5
330	1	0.5	0.5
340	1	0.5	0.5
350	1	0.5	0.5
416	1	0.5	0.5
53	1	0.5	0.5
65	1	0.5	0.5
75	1	0.5	0.5
90	1	0.5	0.5
“NA”	25	11.9	11.9
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
111	1	0.5	0.5
8	1	0.5	0.5
88	1	0.5	0.5
92	1	0.5	0.5
“NA”	206	98.1	98.1
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*5	1	0.3	0.3
112	1	0.3	0.3
114	1	0.3	0.3
125	1	0.3	0.3
138	1	0.3	0.3
140	1	0.3	0.3
145	2	0.6	0.6
146	1	0.3	0.3
148	1	0.3	0.3
150	4	1.3	1.3
152	1	0.3	0.3
153	1	0.3	0.3
154	1	0.3	0.3
155	3	1.0	1.0
156	1	0.3	0.3
157	2	0.6	0.6
158	1	0.3	0.3
159	1	0.3	0.3
160	4	1.3	1.3
162	3	1.0	1.0
163	1	0.3	0.3
165	6	1.9	1.9
167	5	1.6	1.6
168	1	0.3	0.3
170	5	1.6	1.6
172	2	0.6	0.6
173	2	0.6	0.6
174	1	0.3	0.3
175	7	2.2	2.2
176	1	0.3	0.3
177	3	1.0	1.0
178	5	1.6	1.6
179	1	0.3	0.3
180	10	3.2	3.2
182	5	1.6	1.6
184	4	1.3	1.3
185	10	3.2	3.2
187	2	0.6	0.6
188	1	0.3	0.3
189	5	1.6	1.6
190	8	2.5	2.5
192	2	0.6	0.6
193	2	0.6	0.6
195	9	2.9	2.9
196	2	0.6	0.6
197	1	0.3	0.3
198	1	0.3	0.3
199	3	1.0	1.0
2	1	0.3	0.3
2 *	1	0.3	0.3
2 2	1	0.3	0.3
200	10	3.2	3.2
202	3	1.0	1.0
205	6	1.9	1.9
206	1	0.3	0.3
207	3	1.0	1.0
208	5	1.6	1.6
209	3	1.0	1.0
210	10	3.2	3.2
211	1	0.3	0.3
212	1	0.3	0.3
214	2	0.6	0.6
215	9	2.9	2.9
219	2	0.6	0.6
220	9	2.9	2.9
222	2	0.6	0.6
223	1	0.3	0.3
225	1	0.3	0.3
226	3	1.0	1.0
228	1	0.3	0.3
230	3	1.0	1.0
232	2	0.6	0.6
233	1	0.3	0.3
234	1	0.3	0.3
235	3	1.0	1.0
237	1	0.3	0.3
238	2	0.6	0.6
239	1	0.3	0.3
240	6	1.9	1.9
242	2	0.6	0.6
243	1	0.3	0.3
244	1	0.3	0.3
245	4	1.3	1.3
249	1	0.3	0.3
250	3	1.0	1.0
251	1	0.3	0.3
252	1	0.3	0.3
254	2	0.6	0.6
260	2	0.6	0.6
261	1	0.3	0.3
262	2	0.6	0.6
263	1	0.3	0.3
265	6	1.9	1.9
270	1	0.3	0.3
273	1	0.3	0.3
275	3	1.0	1.0
280	2	0.6	0.6
285	1	0.3	0.3
287	1	0.3	0.3
289	1	0.3	0.3
290	2	0.6	0.6
294	1	0.3	0.3
300	1	0.3	0.3
305	1	0.3	0.3
308	1	0.3	0.3
325	1	0.3	0.3
355	1	0.3	0.3
440	1	0.3	0.3
65	1	0.3	0.3
81	1	0.3	0.3
“NA”	33	10.5	10.5
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
0	2	0.6	0.6
2	1	0.3	0.3
75	1	0.3	0.3
“NA”	311	98.7	98.7
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*0	1	0.3	0.3
*2	1	0.3	0.3
*4	1	0.3	0.3
0*	1	0.3	0.3
1	2	0.6	0.6
1*	1	0.3	0.3
115	2	0.6	0.6
120	1	0.3	0.3
130	4	1.1	1.1
135	2	0.6	0.6
137	2	0.6	0.6
138	1	0.3	0.3
139	1	0.3	0.3
140	3	0.8	0.8
141	1	0.3	0.3
143	1	0.3	0.3
145	2	0.6	0.6
146	1	0.3	0.3
147	2	0.6	0.6
150	1	0.3	0.3
152	1	0.3	0.3
153	3	0.8	0.8
154	1	0.3	0.3
155	6	1.7	1.7
156	1	0.3	0.3
157	2	0.6	0.6
158	2	0.6	0.6
159	1	0.3	0.3
160	6	1.7	1.7
161	1	0.3	0.3
162	1	0.3	0.3
163	1	0.3	0.3
164	1	0.3	0.3
165	3	0.8	0.8
166	2	0.6	0.6
168	2	0.6	0.6
169	1	0.3	0.3
170	7	2.0	2.0
175	6	1.7	1.7
176	1	0.3	0.3
177	2	0.6	0.6
178	3	0.8	0.8
180	6	1.7	1.7
182	4	1.1	1.1
183	5	1.4	1.4
184	1	0.3	0.3
185	15	4.2	4.2
186	2	0.6	0.6
187	1	0.3	0.3
188	1	0.3	0.3
189	3	0.8	0.8
190	9	2.5	2.5
191	1	0.3	0.3
192	1	0.3	0.3
193	3	0.8	0.8
194	2	0.6	0.6
195	4	1.1	1.1
196	1	0.3	0.3
197	3	0.8	0.8
198	2	0.6	0.6
199	1	0.3	0.3
2	1	0.3	0.3
200	7	2.0	2.0
201	1	0.3	0.3
203	3	0.8	0.8
205	3	0.8	0.8
206	2	0.6	0.6
207	3	0.8	0.8
208	4	1.1	1.1
209	1	0.3	0.3
210	8	2.2	2.2
211	1	0.3	0.3
212	4	1.1	1.1
213	1	0.3	0.3
214	1	0.3	0.3
215	13	3.7	3.7
216	1	0.3	0.3
218	4	1.1	1.1
220	11	3.1	3.1
221	1	0.3	0.3
222	1	0.3	0.3
223	1	0.3	0.3
224	1	0.3	0.3
225	3	0.8	0.8
230	14	3.9	3.9
232	2	0.6	0.6
233	2	0.6	0.6
234	2	0.6	0.6
235	4	1.1	1.1
237	2	0.6	0.6
240	6	1.7	1.7
241	1	0.3	0.3
242	1	0.3	0.3
243	1	0.3	0.3
244	1	0.3	0.3
245	4	1.1	1.1
246	1	0.3	0.3
247	2	0.6	0.6
250	4	1.1	1.1
255	3	0.8	0.8
257	1	0.3	0.3
260	8	2.2	2.2
261	1	0.3	0.3
262	2	0.6	0.6
264	1	0.3	0.3
265	2	0.6	0.6
266	2	0.6	0.6
268	1	0.3	0.3
270	2	0.6	0.6
271	1	0.3	0.3
275	3	0.8	0.8
277	1	0.3	0.3
280	3	0.8	0.8
281	1	0.3	0.3
285	1	0.3	0.3
287	1	0.3	0.3
295	2	0.6	0.6
298	1	0.3	0.3
315	1	0.3	0.3
317	1	0.3	0.3
350	1	0.3	0.3
361	1	0.3	0.3
375	1	0.3	0.3
376	1	0.3	0.3
397	1	0.3	0.3
424	1	0.3	0.3
68	1	0.3	0.3
72	1	0.3	0.3
74	1	0.3	0.3
76	1	0.3	0.3
“NA”	34	9.6	9.6
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
10	1	0.3	0.3
2	1	0.3	0.3
55	1	0.3	0.3
61	1	0.3	0.3
“NA”	352	98.9	98.9
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*5	1	0.2	0.2
1	2	0.3	0.3
1*	1	0.2	0.2
111	1	0.2	0.2
12	1	0.2	0.2
120	2	0.3	0.3
124	1	0.2	0.2
126	1	0.2	0.2
127	1	0.2	0.2
130	5	0.9	0.9
132	1	0.2	0.2
135	1	0.2	0.2
137	1	0.2	0.2
138	2	0.3	0.3
139	1	0.2	0.2
140	2	0.3	0.3
141	1	0.2	0.2
145	4	0.7	0.7
147	2	0.3	0.3
148	3	0.5	0.5
149	2	0.3	0.3
150	9	1.5	1.5
151	1	0.2	0.2
152	2	0.3	0.3
153	1	0.2	0.2
155	6	1.0	1.0
156	3	0.5	0.5
157	2	0.3	0.3
158	1	0.2	0.2
160	10	1.7	1.7
161	1	0.2	0.2
162	2	0.3	0.3
163	2	0.3	0.3
164	2	0.3	0.3
165	9	1.5	1.5
167	5	0.9	0.9
168	2	0.3	0.3
169	2	0.3	0.3
170	9	1.5	1.5
173	3	0.5	0.5
174	6	1.0	1.0
175	13	2.2	2.2
176	2	0.3	0.3
177	1	0.2	0.2
178	4	0.7	0.7
179	1	0.2	0.2
180	24	4.1	4.1
181	1	0.2	0.2
182	2	0.3	0.3
183	5	0.9	0.9
184	3	0.5	0.5
185	17	2.9	2.9
186	2	0.3	0.3
187	4	0.7	0.7
188	6	1.0	1.0
189	3	0.5	0.5
190	14	2.4	2.4
191	1	0.2	0.2
192	3	0.5	0.5
193	3	0.5	0.5
194	2	0.3	0.3
195	11	1.9	1.9
196	4	0.7	0.7
197	4	0.7	0.7
198	11	1.9	1.9
199	4	0.7	0.7
2	2	0.3	0.3
200	10	1.7	1.7
202	7	1.2	1.2
203	3	0.5	0.5
204	6	1.0	1.0
205	17	2.9	2.9
206	4	0.7	0.7
207	4	0.7	0.7
208	2	0.3	0.3
210	16	2.7	2.7
211	2	0.3	0.3
212	7	1.2	1.2
214	8	1.4	1.4
215	9	1.5	1.5
216	1	0.2	0.2
217	2	0.3	0.3
218	5	0.9	0.9
219	1	0.2	0.2
220	10	1.7	1.7
221	2	0.3	0.3
222	2	0.3	0.3
224	3	0.5	0.5
225	10	1.7	1.7
226	1	0.2	0.2
227	2	0.3	0.3
228	2	0.3	0.3
229	2	0.3	0.3
230	15	2.6	2.6
232	1	0.2	0.2
233	5	0.9	0.9
234	5	0.9	0.9
235	2	0.3	0.3
236	2	0.3	0.3
237	2	0.3	0.3
238	3	0.5	0.5
240	12	2.1	2.1
242	2	0.3	0.3
245	5	0.9	0.9
246	3	0.5	0.5
247	1	0.2	0.2
248	1	0.2	0.2
250	5	0.9	0.9
251	2	0.3	0.3
252	1	0.2	0.2
254	3	0.5	0.5
255	2	0.3	0.3
258	2	0.3	0.3
259	2	0.3	0.3
260	7	1.2	1.2
262	2	0.3	0.3
265	1	0.2	0.2
267	1	0.2	0.2
269	1	0.2	0.2
270	3	0.5	0.5
271	1	0.2	0.2
273	1	0.2	0.2
275	3	0.5	0.5
276	1	0.2	0.2
277	1	0.2	0.2
280	1	0.2	0.2
285	2	0.3	0.3
288	1	0.2	0.2
290	4	0.7	0.7
292	1	0.2	0.2
295	1	0.2	0.2
296	1	0.2	0.2
300	3	0.5	0.5
303	1	0.2	0.2
307	1	0.2	0.2
310	2	0.3	0.3
316	1	0.2	0.2
320	3	0.5	0.5
326	1	0.2	0.2
330	2	0.3	0.3
334	1	0.2	0.2
335	2	0.3	0.3
340	1	0.2	0.2
350	1	0.2	0.2
362	1	0.2	0.2
400	1	0.2	0.2
415	1	0.2	0.2
430	1	0.2	0.2
89	1	0.2	0.2
90	1	0.2	0.2
“NA”	52	8.9	8.9
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
0	1	0.2	0.2
50	1	0.2	0.2
55	1	0.2	0.2
6	1	0.2	0.2
60	1	0.2	0.2
64	1	0.2	0.2
85	1	0.2	0.2
86	1	0.2	0.2
9	2	0.3	0.3
“NA”	575	98.3	98.3
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
*	1	0.1	0.1
* 3	1	0.1	0.1
* 4	1	0.1	0.1
* 5	1	0.1	0.1
* 9	1	0.1	0.1
*35	1	0.1	0.1
*4	1	0.1	0.1
0	2	0.1	0.1
1	3	0.2	0.2
1 *	3	0.2	0.2
1 8	1	0.1	0.1
1*	1	0.1	0.1
106	1	0.1	0.1
110	2	0.1	0.1
117	1	0.1	0.1
119	1	0.1	0.1
120	2	0.1	0.1
121	1	0.1	0.1
122	1	0.1	0.1
125	3	0.2	0.2
130	4	0.2	0.2
133	1	0.1	0.1
134	1	0.1	0.1
135	6	0.3	0.3
137	3	0.2	0.2
138	1	0.1	0.1
139	2	0.1	0.1
14*	1	0.1	0.1
140	9	0.5	0.5
141	1	0.1	0.1
142	5	0.3	0.3
143	1	0.1	0.1
144	1	0.1	0.1
145	4	0.2	0.2
146	2	0.1	0.1
147	4	0.2	0.2
148	3	0.2	0.2
150	18	1.0	1.0
152	3	0.2	0.2
153	2	0.1	0.1
154	7	0.4	0.4
155	18	1.0	1.0
156	6	0.3	0.3
157	2	0.1	0.1
158	9	0.5	0.5
159	5	0.3	0.3
160	25	1.4	1.4
161	4	0.2	0.2
162	10	0.6	0.6
163	6	0.3	0.3
164	5	0.3	0.3
165	27	1.5	1.5
166	3	0.2	0.2
167	7	0.4	0.4
168	13	0.7	0.7
169	6	0.3	0.3
170	40	2.3	2.3
171	8	0.5	0.5
172	13	0.7	0.7
173	8	0.5	0.5
174	7	0.4	0.4
175	32	1.8	1.8
176	11	0.6	0.6
177	4	0.2	0.2
178	14	0.8	0.8
179	5	0.3	0.3
18	2	0.1	0.1
180	31	1.8	1.8
181	5	0.3	0.3
182	13	0.7	0.7
183	7	0.4	0.4
184	6	0.3	0.3
185	34	1.9	1.9
186	8	0.5	0.5
187	11	0.6	0.6
188	18	1.0	1.0
189	13	0.7	0.7
190	50	2.9	2.9
191	5	0.3	0.3
192	13	0.7	0.7
193	11	0.6	0.6
194	7	0.4	0.4
195	28	1.6	1.6
196	8	0.5	0.5
197	13	0.7	0.7
198	19	1.1	1.1
199	8	0.5	0.5
2	6	0.3	0.3
2 1	1	0.1	0.1
2 6	1	0.1	0.1
2*	2	0.1	0.1
200	49	2.8	2.8
201	2	0.1	0.1
202	7	0.4	0.4
203	5	0.3	0.3
204	13	0.7	0.7
205	34	1.9	1.9
206	5	0.3	0.3
207	9	0.5	0.5
208	9	0.5	0.5
209	9	0.5	0.5
210	46	2.6	2.6
211	3	0.2	0.2
212	15	0.9	0.9
213	4	0.2	0.2
214	12	0.7	0.7
215	53	3.0	3.0
216	2	0.1	0.1
217	6	0.3	0.3
218	16	0.9	0.9
219	5	0.3	0.3
220	41	2.3	2.3
221	5	0.3	0.3
222	8	0.5	0.5
223	11	0.6	0.6
224	5	0.3	0.3
225	27	1.5	1.5
226	2	0.1	0.1
227	5	0.3	0.3
228	7	0.4	0.4
229	5	0.3	0.3
230	30	1.7	1.7
231	2	0.1	0.1
232	7	0.4	0.4
233	2	0.1	0.1
234	6	0.3	0.3
235	25	1.4	1.4
236	3	0.2	0.2
237	5	0.3	0.3
238	3	0.2	0.2
239	1	0.1	0.1
240	27	1.5	1.5
241	1	0.1	0.1
242	8	0.5	0.5
243	3	0.2	0.2
244	2	0.1	0.1
245	22	1.3	1.3
246	6	0.3	0.3
247	7	0.4	0.4
248	4	0.2	0.2
249	2	0.1	0.1
250	32	1.8	1.8
251	2	0.1	0.1
252	6	0.3	0.3
253	3	0.2	0.2
254	5	0.3	0.3
255	6	0.3	0.3
256	2	0.1	0.1
257	3	0.2	0.2
258	2	0.1	0.1
259	1	0.1	0.1
260	19	1.1	1.1
261	1	0.1	0.1
262	3	0.2	0.2
263	3	0.2	0.2
264	3	0.2	0.2
265	12	0.7	0.7
266	2	0.1	0.1
267	3	0.2	0.2
268	1	0.1	0.1
270	12	0.7	0.7
272	3	0.2	0.2
274	1	0.1	0.1
275	1	0.1	0.1
276	2	0.1	0.1
277	2	0.1	0.1
278	3	0.2	0.2
279	2	0.1	0.1
280	11	0.6	0.6
282	1	0.1	0.1
284	2	0.1	0.1
285	2	0.1	0.1
286	1	0.1	0.1
287	1	0.1	0.1
289	4	0.2	0.2
29	1	0.1	0.1
290	3	0.2	0.2
292	1	0.1	0.1
294	1	0.1	0.1
295	4	0.2	0.2
297	5	0.3	0.3
298	3	0.2	0.2
3	1	0.1	0.1
300	8	0.5	0.5
302	1	0.1	0.1
305	1	0.1	0.1
307	1	0.1	0.1
309	1	0.1	0.1
310	3	0.2	0.2
314	1	0.1	0.1
315	2	0.1	0.1
317	1	0.1	0.1
319	1	0.1	0.1
320	3	0.2	0.2
321	1	0.1	0.1
324	1	0.1	0.1
325	2	0.1	0.1
330	4	0.2	0.2
335	2	0.1	0.1
340	1	0.1	0.1
344	1	0.1	0.1
358	1	0.1	0.1
360	1	0.1	0.1
365	1	0.1	0.1
370	1	0.1	0.1
400	1	0.1	0.1
410	1	0.1	0.1
50	1	0.1	0.1
60	1	0.1	0.1
65	1	0.1	0.1
7	1	0.1	0.1
71	1	0.1	0.1
78	1	0.1	0.1
80	2	0.1	0.1
84	1	0.1	0.1
92	1	0.1	0.1
97	1	0.1	0.1
98	1	0.1	0.1
“NA”	223	12.7	12.7
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
0	7	0.4	0.4
1	2	0.1	0.1
1 7	1	0.1	0.1
110	1	0.1	0.1
111	1	0.1	0.1
113	1	0.1	0.1
175	1	0.1	0.1
2	2	0.1	0.1
22	1	0.1	0.1
3	1	0.1	0.1
37	1	0.1	0.1
45	1	0.1	0.1
5	1	0.1	0.1
50	2	0.1	0.1
65	1	0.1	0.1
7	1	0.1	0.1
76	1	0.1	0.1
82	1	0.1	0.1
90	1	0.1	0.1
91	1	0.1	0.1
“NA”	1725	98.3	98.3
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f2lbs")

f2lbs
	n	%	val%
140	1	6.2	6.2
179	1	6.2	6.2
185	2	12.5	12.5
187	1	6.2	6.2
188	1	6.2	6.2
189	1	6.2	6.2
190	1	6.2	6.2
200	1	6.2	6.2
207	1	6.2	6.2
210	1	6.2	6.2
242	1	6.2	6.2
243	1	6.2	6.2
259	1	6.2	6.2
275	1	6.2	6.2
“NA”	1	6.2	6.2
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")

How much do you current weight? (kgs)
	n	%	val%
“NA”	16	100	100
Total	16	100	100

rm(temp.dd)

F3

F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
- 4=5-7 times per week
- 3=3-4 times per week
- 2=1-2 times per week
- 1=Less than once per week/do not exercise

All data

  f3 <- as.factor(d[,"f3"])
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1",
                     Scantron_Error="*")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1","Scantron_Error"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	580	16.3	17.6	16.3	17.6
Per_week_3_4	960	27.0	29.1	43.3	46.7
Per_week_1_2	934	26.3	28.3	69.6	75.0
Per_week_less_1	823	23.1	24.9	92.7	99.9
Scantron_Error	3	0.1	0.1	92.8	100.0
NA	257	7.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	88	27.4	27.8	27.4	27.8
Per_week_3_4	96	29.9	30.4	57.3	58.2
Per_week_1_2	80	24.9	25.3	82.2	83.5
Per_week_less_1	52	16.2	16.5	98.4	100.0
Scantron_Error	0	0.0	0.0	98.4	100.0
NA	5	1.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	39	18.6	19.6	18.6	19.6
Per_week_3_4	75	35.7	37.7	54.3	57.3
Per_week_1_2	54	25.7	27.1	80.0	84.4
Per_week_less_1	31	14.8	15.6	94.8	100.0
Scantron_Error	0	0.0	0.0	94.8	100.0
NA	11	5.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	60	19.0	20.8	19.0	20.8
Per_week_3_4	92	29.2	31.9	48.3	52.8
Per_week_1_2	83	26.3	28.8	74.6	81.6
Per_week_less_1	53	16.8	18.4	91.4	100.0
Scantron_Error	0	0.0	0.0	91.4	100.0
NA	27	8.6	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	47	13.2	14.4	13.2	14.4
Per_week_3_4	78	21.9	23.9	35.1	38.3
Per_week_1_2	100	28.1	30.7	63.2	69.0
Per_week_less_1	101	28.4	31.0	91.6	100.0
Scantron_Error	0	0.0	0.0	91.6	100.0
NA	30	8.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	84	14.4	15.6	14.4	15.6
Per_week_3_4	141	24.1	26.2	38.5	41.8
Per_week_1_2	154	26.3	28.6	64.8	70.4
Per_week_less_1	159	27.2	29.6	92.0	100.0
Scantron_Error	0	0.0	0.0	92.0	100.0
NA	47	8.0	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	260	14.8	16.0	14.8	16.0
Per_week_3_4	476	27.1	29.4	42.0	45.4
Per_week_1_2	457	26.1	28.2	68.0	73.6
Per_week_less_1	425	24.2	26.2	92.2	99.8
Scantron_Error	3	0.2	0.2	92.4	100.0
NA	133	7.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")

F3. How many days per week do you typically get moderate or strenuous exercise
	n	%	val%	%cum	val%cum
Per_week_5_7	2	12.5	16.7	12.5	16.7
Per_week_3_4	2	12.5	16.7	25.0	33.3
Per_week_1_2	6	37.5	50.0	62.5	83.3
Per_week_less_1	2	12.5	16.7	75.0	100.0
Scantron_Error	0	0.0	0.0	75.0	100.0
NA	4	25.0	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

rm(temp.dd)

F4

F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
- 2=Less than 30 minutes
- 3=30 minutes – 1 hour
- 4=More than 1 hour
- 1=Do not exercise

All data

  f4 <- as.factor(d[,"f4"])
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1",
                     Scantron_Error="*")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise","Scantron_Error"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")

F4
	n	%	val%	%cum	val%cum
Less_than_30_min	589	16.6	17.9	16.6	17.9
Between_30_min_1_hour	1363	38.3	41.4	54.9	59.3
More_than_1_hour	734	20.6	22.3	75.5	81.6
Do_not_exercise	603	17.0	18.3	92.5	99.9
Scantron_Error	4	0.1	0.1	92.6	100.0
NA	264	7.4	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	37	11.5	11.8	11.5	11.8
Between_30_min_1_hour	158	49.2	50.3	60.7	62.1
More_than_1_hour	83	25.9	26.4	86.6	88.5
Do_not_exercise	35	10.9	11.1	97.5	99.7
Scantron_Error	1	0.3	0.3	97.8	100.0
NA	7	2.2	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	33	15.7	16.8	15.7	16.8
Between_30_min_1_hour	90	42.9	45.7	58.6	62.4
More_than_1_hour	61	29.0	31.0	87.6	93.4
Do_not_exercise	13	6.2	6.6	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	13	6.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	51	16.2	17.8	16.2	17.8
Between_30_min_1_hour	122	38.7	42.5	54.9	60.3
More_than_1_hour	78	24.8	27.2	79.7	87.5
Do_not_exercise	36	11.4	12.5	91.1	100.0
Scantron_Error	0	0.0	0.0	91.1	100.0
NA	28	8.9	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	84	23.6	25.8	23.6	25.8
Between_30_min_1_hour	118	33.1	36.3	56.7	62.2
More_than_1_hour	62	17.4	19.1	74.2	81.2
Do_not_exercise	61	17.1	18.8	91.3	100.0
Scantron_Error	0	0.0	0.0	91.3	100.0
NA	31	8.7	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	97	16.6	18.1	16.6	18.1
Between_30_min_1_hour	197	33.7	36.7	50.3	54.7
More_than_1_hour	111	19.0	20.7	69.2	75.4
Do_not_exercise	131	22.4	24.4	91.6	99.8
Scantron_Error	1	0.2	0.2	91.8	100.0
NA	48	8.2	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	286	16.3	17.6	16.3	17.6
Between_30_min_1_hour	669	38.1	41.3	54.4	58.9
More_than_1_hour	338	19.3	20.9	73.7	79.8
Do_not_exercise	326	18.6	20.1	92.3	99.9
Scantron_Error	2	0.1	0.1	92.4	100.0
NA	133	7.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f4")

f4
	n	%	val%	%cum	val%cum
Less_than_30_min	1	6.2	8.3	6.2	8.3
Between_30_min_1_hour	9	56.2	75.0	62.5	83.3
More_than_1_hour	1	6.2	8.3	68.8	91.7
Do_not_exercise	1	6.2	8.3	75.0	100.0
Scantron_Error	0	0.0	0.0	75.0	100.0
NA	4	25.0	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

rm(temp.dd)

F5

F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
- 6=Everyday
- 5=5-6 times per week
- 4=3-4 times per week
- 3=1-2 times per week
- 2=Fewer than once per week
- 1=Did not drink

All data

  f5 <- as.factor(d[,"f5"])
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1",
                     Scantron_Error="*")
  f5 <- ordered(f5, c("Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink","Scantron_Error"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	135	3.8	4.1	3.8	4.1
Per_week_3_4_times	390	11.0	11.8	14.8	15.9
Per_week_1_2_times	518	14.6	15.7	29.3	31.6
Per_week_fewer_once	668	18.8	20.2	48.1	51.8
Not_drink	1588	44.6	48.0	92.7	99.8
Scantron_Error	6	0.2	0.2	92.9	100.0
NA	252	7.1	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	16	5.0	5.2	5.0	5.2
Per_week_3_4_times	40	12.5	12.9	17.4	18.1
Per_week_1_2_times	42	13.1	13.5	30.5	31.6
Per_week_fewer_once	65	20.2	21.0	50.8	52.6
Not_drink	147	45.8	47.4	96.6	100.0
Scantron_Error	0	0.0	0.0	96.6	100.0
NA	11	3.4	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	14	6.7	7.3	6.7	7.3
Per_week_3_4_times	22	10.5	11.5	17.1	18.8
Per_week_1_2_times	37	17.6	19.4	34.8	38.2
Per_week_fewer_once	33	15.7	17.3	50.5	55.5
Not_drink	85	40.5	44.5	91.0	100.0
Scantron_Error	0	0.0	0.0	91.0	100.0
NA	19	9.0	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	13	4.1	4.5	4.1	4.5
Per_week_3_4_times	44	14.0	15.2	18.1	19.7
Per_week_1_2_times	36	11.4	12.4	29.5	32.1
Per_week_fewer_once	60	19.0	20.7	48.6	52.8
Not_drink	136	43.2	46.9	91.7	99.7
Scantron_Error	1	0.3	0.3	92.1	100.0
NA	25	7.9	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	10	2.8	3.0	2.8	3.0
Per_week_3_4_times	42	11.8	12.6	14.6	15.6
Per_week_1_2_times	65	18.3	19.5	32.9	35.1
Per_week_fewer_once	63	17.7	18.9	50.6	54.1
Not_drink	151	42.4	45.3	93.0	99.4
Scantron_Error	2	0.6	0.6	93.5	100.0
NA	23	6.5	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	25	4.3	4.6	4.3	4.6
Per_week_3_4_times	65	11.1	12.0	15.4	16.6
Per_week_1_2_times	90	15.4	16.6	30.8	33.3
Per_week_fewer_once	99	16.9	18.3	47.7	51.6
Not_drink	261	44.6	48.2	92.3	99.8
Scantron_Error	1	0.2	0.2	92.5	100.0
NA	44	7.5	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	57	3.2	3.5	3.2	3.5
Per_week_3_4_times	174	9.9	10.7	13.2	14.2
Per_week_1_2_times	247	14.1	15.2	27.3	29.4
Per_week_fewer_once	347	19.8	21.4	47.0	50.8
Not_drink	798	45.5	49.1	92.5	99.9
Scantron_Error	2	0.1	0.1	92.6	100.0
NA	129	7.4	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")

f5
	n	%	val%	%cum	val%cum
Per_week_5_6_times	0	0.0	0.0	0.0	0.0
Per_week_3_4_times	3	18.8	20.0	18.8	20.0
Per_week_1_2_times	1	6.2	6.7	25.0	26.7
Per_week_fewer_once	1	6.2	6.7	31.2	33.3
Not_drink	10	62.5	66.7	93.8	100.0
Scantron_Error	0	0.0	0.0	93.8	100.0
NA	1	6.2	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

rm(temp.dd)

F6

F6. When you drank during the past month, how many drinks do you have on a typical occasion?
- 3=3 or more drinks
- 2=1-2 drinks
- 1=Did not drink

All data

  f6 <- as.factor(d[,"f6"])
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1",
                     Scantron_Error="*")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink","Scantron_Error"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	332	9.3	10.5	9.3	10.5
One_to_two_drinks	1467	41.2	46.3	50.6	56.8
Not_drink	1368	38.5	43.2	89.0	100.0
Scantron_Error	1	0.0	0.0	89.1	100.0
NA	389	10.9	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	22	6.9	7.0	6.9	7.0
One_to_two_drinks	141	43.9	44.9	50.8	51.9
Not_drink	151	47.0	48.1	97.8	100.0
Scantron_Error	0	0.0	0.0	97.8	100.0
NA	7	2.2	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	17	8.1	9.1	8.1	9.1
One_to_two_drinks	103	49.0	55.4	57.1	64.5
Not_drink	66	31.4	35.5	88.6	100.0
Scantron_Error	0	0.0	0.0	88.6	100.0
NA	24	11.4	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	36	11.4	12.6	11.4	12.6
One_to_two_drinks	132	41.9	46.3	53.3	58.9
Not_drink	117	37.1	41.1	90.5	100.0
Scantron_Error	0	0.0	0.0	90.5	100.0
NA	30	9.5	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	32	9.0	10.4	9.0	10.4
One_to_two_drinks	151	42.4	49.2	51.4	59.6
Not_drink	124	34.8	40.4	86.2	100.0
Scantron_Error	0	0.0	0.0	86.2	100.0
NA	49	13.8	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	74	12.6	13.5	12.6	13.5
One_to_two_drinks	225	38.5	41.1	51.1	54.7
Not_drink	248	42.4	45.3	93.5	100.0
Scantron_Error	0	0.0	0.0	93.5	100.0
NA	38	6.5	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	150	8.6	9.9	8.6	9.9
One_to_two_drinks	711	40.5	46.9	49.1	56.8
Not_drink	654	37.3	43.1	86.4	99.9
Scantron_Error	1	0.1	0.1	86.4	100.0
NA	238	13.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")

f6
	n	%	val%	%cum	val%cum
Three_or_more	1	6.2	7.7	6.2	7.7
One_to_two_drinks	4	25.0	30.8	31.2	38.5
Not_drink	8	50.0	61.5	81.2	100.0
Scantron_Error	0	0.0	0.0	81.2	100.0
NA	3	18.8	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

rm(temp.dd)

F7

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
- 1=No
- 2=Yes
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
- 555 = “Less than 10”
- 777 = “75+”
F7a. How many cigarettes do you (or did you) usually smoke per day?
- 1=1-5
- 2=6-10
- 3=11-20
- 4=21-30
- 5=31+
F7b. Have you quit smoking?
- 1=No
- 2=Yes
F7BAge. If yes, At what age did you quit?
- 555 = “Less than 10”
- 777 = “75+”

All data

  f7 <- as.factor(d[,"f7"])
  levels(f7) <- list(Yes="2",
                     No="1",
                     Scantron_Error="*")
  f7 <- ordered(f7, c("No","Yes","Scantron_Error"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	1806	50.8	53.6	50.8	53.6
Yes	1565	44.0	46.4	94.8	100.0
Scantron_Error	0	0.0	0.0	94.8	100.0
NA	186	5.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
0	5	0.1	0.1
1	2	0.1	0.1
10	12	0.3	0.3
11	6	0.2	0.2
12	25	0.7	0.7
13	40	1.1	1.1
14	47	1.3	1.3
15	111	3.1	3.1
16	130	3.7	3.7
17	127	3.6	3.6
18	198	5.6	5.6
19	107	3.0	3.0
20	111	3.1	3.1
21	61	1.7	1.7
22	47	1.3	1.3
23	29	0.8	0.8
24	15	0.4	0.4
25	42	1.2	1.2
26	9	0.3	0.3
27	13	0.4	0.4
28	14	0.4	0.4
29	2	0.1	0.1
3	1	0.0	0.0
30	30	0.8	0.8
31	2	0.1	0.1
32	6	0.2	0.2
33	3	0.1	0.1
34	5	0.1	0.1
35	18	0.5	0.5
36	1	0.0	0.0
38	2	0.1	0.1
4	1	0.0	0.0
40	7	0.2	0.2
41	1	0.0	0.0
42	1	0.0	0.0
44	1	0.0	0.0
45	3	0.1	0.1
48	1	0.0	0.0
49	3	0.1	0.1
5	1	0.0	0.0
50	3	0.1	0.1
54	2	0.1	0.1
55	1	0.0	0.0
56	1	0.0	0.0
59	1	0.0	0.0
60	1	0.0	0.0
62	1	0.0	0.0
7	2	0.1	0.1
8	2	0.1	0.1
9	4	0.1	0.1
Less_than_10	2	0.1	0.1
“NA”	2297	64.6	64.6
Total	3557	100.0	100.0

  f7a <- as.factor(d[,"f7a"])
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5",
                     Scantron_Error="*")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31","Scantron_Error"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	569	16.0	35.7	16.0	35.7
Six_to_ten	492	13.8	30.8	29.8	66.5
Eleven_to_twenty	387	10.9	24.3	40.7	90.8
Twentyone_to_Thirty	102	2.9	6.4	43.6	97.2
Older_31	44	1.2	2.8	44.8	99.9
Scantron_Error	1	0.0	0.1	44.8	100.0
NA	1962	55.2	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

  f7b <- as.factor(d[,"f7b"])
  levels(f7b) <- list(No="1",
                     Yes="2",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	362	10.2	22.2
Yes	1262	35.5	77.6
Scantron_Error	3	0.1	0.2
NA	1930	54.3	NA
Total	3557	100.0	100.0

  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
1	2	0.1	0.1
12	1	0.0	0.0
15	2	0.1	0.1
16	4	0.1	0.1
17	6	0.2	0.2
18	4	0.1	0.1
19	9	0.3	0.3
20	24	0.7	0.7
21	13	0.4	0.4
22	17	0.5	0.5
23	25	0.7	0.7
24	13	0.4	0.4
25	36	1.0	1.0
26	15	0.4	0.4
27	22	0.6	0.6
28	30	0.8	0.8
29	17	0.5	0.5
30	70	2.0	2.0
31	18	0.5	0.5
32	17	0.5	0.5
33	15	0.4	0.4
34	16	0.4	0.4
35	63	1.8	1.8
36	15	0.4	0.4
37	11	0.3	0.3
38	24	0.7	0.7
39	12	0.3	0.3
4	2	0.1	0.1
40	77	2.2	2.2
41	13	0.4	0.4
42	21	0.6	0.6
43	18	0.5	0.5
44	11	0.3	0.3
45	53	1.5	1.5
46	12	0.3	0.3
47	14	0.4	0.4
48	20	0.6	0.6
49	21	0.6	0.6
50	79	2.2	2.2
51	17	0.5	0.5
52	21	0.6	0.6
53	13	0.4	0.4
54	14	0.4	0.4
55	34	1.0	1.0
56	19	0.5	0.5
57	14	0.4	0.4
58	24	0.7	0.7
59	16	0.4	0.4
6	1	0.0	0.0
60	32	0.9	0.9
61	11	0.3	0.3
62	17	0.5	0.5
63	18	0.5	0.5
64	12	0.3	0.3
65	30	0.8	0.8
66	15	0.4	0.4
67	14	0.4	0.4
68	11	0.3	0.3
69	11	0.3	0.3
7	1	0.0	0.0
70	12	0.3	0.3
71	3	0.1	0.1
72	1	0.0	0.0
73	2	0.1	0.1
74	1	0.0	0.0
75	1	0.0	0.0
76	2	0.1	0.1
78	1	0.0	0.0
8	2	0.1	0.1
9	1	0.0	0.0
“NA”	2344	65.9	65.9
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	166	51.7	52.5	51.7	52.5
Yes	150	46.7	47.5	98.4	100.0
Scantron_Error	0	0.0	0.0	98.4	100.0
NA	5	1.6	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
10	2	0.6	0.6
12	2	0.6	0.6
13	6	1.9	1.9
14	3	0.9	0.9
15	10	3.1	3.1
16	13	4.0	4.0
17	13	4.0	4.0
18	14	4.4	4.4
19	11	3.4	3.4
20	11	3.4	3.4
21	11	3.4	3.4
22	9	2.8	2.8
23	3	0.9	0.9
25	3	0.9	0.9
26	2	0.6	0.6
27	1	0.3	0.3
28	2	0.6	0.6
30	1	0.3	0.3
32	1	0.3	0.3
33	1	0.3	0.3
34	1	0.3	0.3
35	3	0.9	0.9
40	1	0.3	0.3
45	1	0.3	0.3
48	1	0.3	0.3
“NA”	195	60.7	60.7
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	54	16.8	36.5	16.8	36.5
Six_to_ten	47	14.6	31.8	31.5	68.2
Eleven_to_twenty	37	11.5	25.0	43.0	93.2
Twentyone_to_Thirty	8	2.5	5.4	45.5	98.6
Older_31	2	0.6	1.4	46.1	100.0
Scantron_Error	0	0.0	0.0	46.1	100.0
NA	173	53.9	NA	100.0	NA
Total	321	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	20	6.2	13.6
Yes	127	39.6	86.4
Scantron_Error	0	0.0	0.0
NA	174	54.2	NA
Total	321	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
19	1	0.3	0.3
20	3	0.9	0.9
21	1	0.3	0.3
23	1	0.3	0.3
25	5	1.6	1.6
26	4	1.2	1.2
27	3	0.9	0.9
28	3	0.9	0.9
29	1	0.3	0.3
30	6	1.9	1.9
31	2	0.6	0.6
32	2	0.6	0.6
33	1	0.3	0.3
34	4	1.2	1.2
35	5	1.6	1.6
36	1	0.3	0.3
37	1	0.3	0.3
38	2	0.6	0.6
39	2	0.6	0.6
40	8	2.5	2.5
42	4	1.2	1.2
43	3	0.9	0.9
44	2	0.6	0.6
45	8	2.5	2.5
47	1	0.3	0.3
48	2	0.6	0.6
49	1	0.3	0.3
50	8	2.5	2.5
51	2	0.6	0.6
52	8	2.5	2.5
53	2	0.6	0.6
54	2	0.6	0.6
55	2	0.6	0.6
56	2	0.6	0.6
57	4	1.2	1.2
58	4	1.2	1.2
59	2	0.6	0.6
6	1	0.3	0.3
60	3	0.9	0.9
62	2	0.6	0.6
63	2	0.6	0.6
64	3	0.9	0.9
65	2	0.6	0.6
66	2	0.6	0.6
67	1	0.3	0.3
70	2	0.6	0.6
“NA”	190	59.2	59.2
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	102	48.6	51.3	48.6	51.3
Yes	97	46.2	48.7	94.8	100.0
Scantron_Error	0	0.0	0.0	94.8	100.0
NA	11	5.2	NA	100.0	NA
Total	210	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7ageE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
X0L	X0L.1	X0L.2	val%
0	0	0	NA

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	39	18.6	39	18.6	39
Six_to_ten	28	13.3	28	31.9	67
Eleven_to_twenty	23	11.0	23	42.9	90
Twentyone_to_Thirty	9	4.3	9	47.1	99
Older_31	1	0.5	1	47.6	100
Scantron_Error	0	0.0	0	47.6	100
NA	110	52.4	NA	100.0	NA
Total	210	100.0	100	100.0	100

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	12	5.7	12.5
Yes	84	40.0	87.5
Scantron_Error	0	0.0	0.0
NA	114	54.3	NA
Total	210	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
16	1	0.5	0.5
20	3	1.4	1.4
21	2	1.0	1.0
24	3	1.4	1.4
25	3	1.4	1.4
26	1	0.5	0.5
27	3	1.4	1.4
28	4	1.9	1.9
30	5	2.4	2.4
31	3	1.4	1.4
32	2	1.0	1.0
33	1	0.5	0.5
35	4	1.9	1.9
36	3	1.4	1.4
37	2	1.0	1.0
38	4	1.9	1.9
39	2	1.0	1.0
40	5	2.4	2.4
42	2	1.0	1.0
43	2	1.0	1.0
45	5	2.4	2.4
46	1	0.5	0.5
47	1	0.5	0.5
49	2	1.0	1.0
50	3	1.4	1.4
51	1	0.5	0.5
52	3	1.4	1.4
56	1	0.5	0.5
58	1	0.5	0.5
59	2	1.0	1.0
60	1	0.5	0.5
63	2	1.0	1.0
65	2	1.0	1.0
66	3	1.4	1.4
70	1	0.5	0.5
“NA”	126	60.0	60.0
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	173	54.9	57.5	54.9	57.5
Yes	128	40.6	42.5	95.6	100.0
Scantron_Error	0	0.0	0.0	95.6	100.0
NA	14	4.4	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
11	1	0.3	0.3
12	1	0.3	0.3
13	1	0.3	0.3
14	4	1.3	1.3
15	8	2.5	2.5
16	9	2.9	2.9
17	8	2.5	2.5
18	15	4.8	4.8
19	12	3.8	3.8
20	9	2.9	2.9
21	6	1.9	1.9
22	6	1.9	1.9
23	3	1.0	1.0
25	6	1.9	1.9
27	1	0.3	0.3
28	3	1.0	1.0
30	2	0.6	0.6
32	1	0.3	0.3
34	1	0.3	0.3
35	2	0.6	0.6
40	1	0.3	0.3
“NA”	215	68.3	68.3
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	47	14.9	37.3	14.9	37.3
Six_to_ten	42	13.3	33.3	28.3	70.6
Eleven_to_twenty	25	7.9	19.8	36.2	90.5
Twentyone_to_Thirty	9	2.9	7.1	39.0	97.6
Older_31	3	1.0	2.4	40.0	100.0
Scantron_Error	0	0.0	0.0	40.0	100.0
NA	189	60.0	NA	100.0	NA
Total	315	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	27	8.6	20.3
Yes	106	33.7	79.7
Scantron_Error	0	0.0	0.0
NA	182	57.8	NA
Total	315	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
12	1	0.3	0.3
16	2	0.6	0.6
17	1	0.3	0.3
20	2	0.6	0.6
21	2	0.6	0.6
22	1	0.3	0.3
23	3	1.0	1.0
24	2	0.6	0.6
25	3	1.0	1.0
27	2	0.6	0.6
28	3	1.0	1.0
29	1	0.3	0.3
30	5	1.6	1.6
31	2	0.6	0.6
32	1	0.3	0.3
33	2	0.6	0.6
34	4	1.3	1.3
35	4	1.3	1.3
38	1	0.3	0.3
39	1	0.3	0.3
40	4	1.3	1.3
41	1	0.3	0.3
42	3	1.0	1.0
44	2	0.6	0.6
45	4	1.3	1.3
46	1	0.3	0.3
47	3	1.0	1.0
48	3	1.0	1.0
49	1	0.3	0.3
50	8	2.5	2.5
51	1	0.3	0.3
52	1	0.3	0.3
53	3	1.0	1.0
54	3	1.0	1.0
55	3	1.0	1.0
56	1	0.3	0.3
57	2	0.6	0.6
58	2	0.6	0.6
59	1	0.3	0.3
60	2	0.6	0.6
61	2	0.6	0.6
64	4	1.3	1.3
65	3	1.0	1.0
67	1	0.3	0.3
72	1	0.3	0.3
“NA”	212	67.3	67.3
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	175	49.2	52.2	49.2	52.2
Yes	160	44.9	47.8	94.1	100.0
Scantron_Error	0	0.0	0.0	94.1	100.0
NA	21	5.9	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
1	1	0.3	0.3
10	1	0.3	0.3
12	5	1.4	1.4
13	3	0.8	0.8
14	8	2.2	2.2
15	12	3.4	3.4
16	15	4.2	4.2
17	11	3.1	3.1
18	19	5.3	5.3
19	10	2.8	2.8
20	9	2.5	2.5
21	3	0.8	0.8
22	2	0.6	0.6
23	3	0.8	0.8
24	1	0.3	0.3
25	6	1.7	1.7
26	1	0.3	0.3
27	1	0.3	0.3
30	2	0.6	0.6
31	1	0.3	0.3
32	1	0.3	0.3
33	1	0.3	0.3
35	1	0.3	0.3
4	1	0.3	0.3
54	1	0.3	0.3
7	1	0.3	0.3
8	1	0.3	0.3
9	1	0.3	0.3
“NA”	234	65.7	65.7
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	53	14.9	31.9	14.9	31.9
Six_to_ten	45	12.6	27.1	27.5	59.0
Eleven_to_twenty	51	14.3	30.7	41.9	89.8
Twentyone_to_Thirty	14	3.9	8.4	45.8	98.2
Older_31	3	0.8	1.8	46.6	100.0
Scantron_Error	0	0.0	0.0	46.6	100.0
NA	190	53.4	NA	100.0	NA
Total	356	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	50	14.0	30.5
Yes	113	31.7	68.9
Scantron_Error	1	0.3	0.6
NA	192	53.9	NA
Total	356	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
1	1	0.3	0.3
17	1	0.3	0.3
18	1	0.3	0.3
22	1	0.3	0.3
23	2	0.6	0.6
24	1	0.3	0.3
25	4	1.1	1.1
26	2	0.6	0.6
27	2	0.6	0.6
28	3	0.8	0.8
30	10	2.8	2.8
32	4	1.1	1.1
34	1	0.3	0.3
35	5	1.4	1.4
36	1	0.3	0.3
37	1	0.3	0.3
38	3	0.8	0.8
39	1	0.3	0.3
40	4	1.1	1.1
41	3	0.8	0.8
43	3	0.8	0.8
44	1	0.3	0.3
45	5	1.4	1.4
46	2	0.6	0.6
49	1	0.3	0.3
50	8	2.2	2.2
52	1	0.3	0.3
53	2	0.6	0.6
55	8	2.2	2.2
56	4	1.1	1.1
57	2	0.6	0.6
58	2	0.6	0.6
59	1	0.3	0.3
60	2	0.6	0.6
61	2	0.6	0.6
62	3	0.8	0.8
63	2	0.6	0.6
65	2	0.6	0.6
66	1	0.3	0.3
68	2	0.6	0.6
69	2	0.6	0.6
71	1	0.3	0.3
78	1	0.3	0.3
9	1	0.3	0.3
“NA”	246	69.1	69.1
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	260	44.4	47.3	44.4	47.3
Yes	290	49.6	52.7	94.0	100.0
Scantron_Error	0	0.0	0.0	94.0	100.0
NA	35	6.0	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
0	1	0.2	0.2
10	3	0.5	0.5
12	4	0.7	0.7
13	6	1.0	1.0
14	9	1.5	1.5
15	22	3.8	3.8
16	25	4.3	4.3
17	30	5.1	5.1
18	38	6.5	6.5
19	16	2.7	2.7
20	16	2.7	2.7
21	11	1.9	1.9
22	8	1.4	1.4
23	1	0.2	0.2
24	2	0.3	0.3
25	6	1.0	1.0
26	1	0.2	0.2
27	1	0.2	0.2
28	7	1.2	1.2
3	1	0.2	0.2
30	6	1.0	1.0
31	1	0.2	0.2
32	2	0.3	0.3
34	1	0.2	0.2
35	3	0.5	0.5
40	1	0.2	0.2
56	1	0.2	0.2
62	1	0.2	0.2
9	2	0.3	0.3
“NA”	359	61.4	61.4
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	86	14.7	29.1	14.7	29.1
Six_to_ten	97	16.6	32.8	31.3	61.8
Eleven_to_twenty	84	14.4	28.4	45.6	90.2
Twentyone_to_Thirty	16	2.7	5.4	48.4	95.6
Older_31	12	2.1	4.1	50.4	99.7
Scantron_Error	1	0.2	0.3	50.6	100.0
NA	289	49.4	NA	100.0	NA
Total	585	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	84	14.4	28.3
Yes	211	36.1	71.0
Scantron_Error	2	0.3	0.7
NA	288	49.2	NA
Total	585	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
17	1	0.2	0.2
19	2	0.3	0.3
20	5	0.9	0.9
21	2	0.3	0.3
22	3	0.5	0.5
23	1	0.2	0.2
25	6	1.0	1.0
26	1	0.2	0.2
27	3	0.5	0.5
28	2	0.3	0.3
29	1	0.2	0.2
30	20	3.4	3.4
31	1	0.2	0.2
32	1	0.2	0.2
33	1	0.2	0.2
34	1	0.2	0.2
35	10	1.7	1.7
36	2	0.3	0.3
38	4	0.7	0.7
39	2	0.3	0.3
4	1	0.2	0.2
40	17	2.9	2.9
41	2	0.3	0.3
42	2	0.3	0.3
43	2	0.3	0.3
44	4	0.7	0.7
45	9	1.5	1.5
46	1	0.2	0.2
47	4	0.7	0.7
48	3	0.5	0.5
49	3	0.5	0.5
50	12	2.1	2.1
51	2	0.3	0.3
54	3	0.5	0.5
55	12	2.1	2.1
56	2	0.3	0.3
57	1	0.2	0.2
58	7	1.2	1.2
60	4	0.7	0.7
61	2	0.3	0.3
62	4	0.7	0.7
63	4	0.7	0.7
64	2	0.3	0.3
65	6	1.0	1.0
66	1	0.2	0.2
67	1	0.2	0.2
68	2	0.3	0.3
69	6	1.0	1.0
7	1	0.2	0.2
70	3	0.5	0.5
71	1	0.2	0.2
75	1	0.2	0.2
8	1	0.2	0.2
“NA”	390	66.7	66.7
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	920	52.5	55.6	52.5	55.6
Yes	736	42.0	44.4	94.4	100.0
Scantron_Error	0	0.0	0.0	94.4	100.0
NA	98	5.6	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
0	3	0.2	0.2
1	1	0.1	0.1
10	6	0.3	0.3
11	5	0.3	0.3
12	11	0.6	0.6
13	20	1.1	1.1
14	21	1.2	1.2
15	58	3.3	3.3
16	56	3.2	3.2
17	64	3.6	3.6
18	104	5.9	5.9
19	46	2.6	2.6
20	58	3.3	3.3
21	25	1.4	1.4
22	17	1.0	1.0
23	15	0.9	0.9
24	8	0.5	0.5
25	19	1.1	1.1
26	3	0.2	0.2
27	9	0.5	0.5
28	2	0.1	0.1
29	2	0.1	0.1
30	18	1.0	1.0
32	1	0.1	0.1
33	1	0.1	0.1
35	8	0.5	0.5
38	2	0.1	0.1
40	3	0.2	0.2
41	1	0.1	0.1
42	1	0.1	0.1
44	1	0.1	0.1
45	1	0.1	0.1
49	3	0.2	0.2
5	1	0.1	0.1
50	3	0.2	0.2
54	1	0.1	0.1
55	1	0.1	0.1
60	1	0.1	0.1
7	1	0.1	0.1
8	1	0.1	0.1
9	1	0.1	0.1
Less_than_10	2	0.1	0.1
“NA”	1149	65.5	65.5
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	284	16.2	37.7	16.2	37.7
Six_to_ten	233	13.3	30.9	29.5	68.7
Eleven_to_twenty	167	9.5	22.2	39.0	90.8
Twentyone_to_Thirty	46	2.6	6.1	41.6	96.9
Older_31	23	1.3	3.1	42.9	100.0
Scantron_Error	0	0.0	0.0	42.9	100.0
NA	1001	57.1	NA	100.0	NA
Total	1754	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	168	9.6	21.4
Yes	616	35.1	78.6
Scantron_Error	0	0.0	0.0
NA	970	55.3	NA
Total	1754	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
1	1	0.1	0.1
15	2	0.1	0.1
16	1	0.1	0.1
17	3	0.2	0.2
18	3	0.2	0.2
19	6	0.3	0.3
20	11	0.6	0.6
21	6	0.3	0.3
22	12	0.7	0.7
23	18	1.0	1.0
24	6	0.3	0.3
25	15	0.9	0.9
26	7	0.4	0.4
27	9	0.5	0.5
28	15	0.9	0.9
29	14	0.8	0.8
30	24	1.4	1.4
31	10	0.6	0.6
32	7	0.4	0.4
33	10	0.6	0.6
34	6	0.3	0.3
35	35	2.0	2.0
36	8	0.5	0.5
37	7	0.4	0.4
38	10	0.6	0.6
39	4	0.2	0.2
4	1	0.1	0.1
40	38	2.2	2.2
41	7	0.4	0.4
42	9	0.5	0.5
43	8	0.5	0.5
44	2	0.1	0.1
45	22	1.3	1.3
46	7	0.4	0.4
47	5	0.3	0.3
48	12	0.7	0.7
49	13	0.7	0.7
50	40	2.3	2.3
51	11	0.6	0.6
52	8	0.5	0.5
53	6	0.3	0.3
54	6	0.3	0.3
55	9	0.5	0.5
56	9	0.5	0.5
57	5	0.3	0.3
58	8	0.5	0.5
59	10	0.6	0.6
60	19	1.1	1.1
61	5	0.3	0.3
62	8	0.5	0.5
63	8	0.5	0.5
64	3	0.2	0.2
65	15	0.9	0.9
66	8	0.5	0.5
67	11	0.6	0.6
68	7	0.4	0.4
69	3	0.2	0.2
70	6	0.3	0.3
71	1	0.1	0.1
73	2	0.1	0.1
74	1	0.1	0.1
76	2	0.1	0.1
8	1	0.1	0.1
“NA”	1168	66.6	66.6
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")

F7. Have you ever smoked at least 100 cigarettes in your lifetime?
	n	%	val%	%cum	val%cum
No	10	62.5	71.4	62.5	71.4
Yes	4	25.0	28.6	87.5	100.0
Scantron_Error	0	0.0	0.0	87.5	100.0
NA	2	12.5	NA	100.0	NA
Total	16	100.0	100.0	100.0	100.0

  result<-questionr::freq(temp.dd$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")

If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
	n	%	val%
16	1	6.2	6.2
19	1	6.2	6.2
21	1	6.2	6.2
23	1	6.2	6.2
“NA”	12	75.0	75.0
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")

F7a. How many cigarettes do you (or did you) usually smoke per day?
	n	%	val%	%cum	val%cum
One_to_five	6	37.5	100	37.5	100
Six_to_ten	0	0.0	0	37.5	100
Eleven_to_twenty	0	0.0	0	37.5	100
Twentyone_to_Thirty	0	0.0	0	37.5	100
Older_31	0	0.0	0	37.5	100
Scantron_Error	0	0.0	0	37.5	100
NA	10	62.5	NA	100.0	NA
Total	16	100.0	100	100.0	100

  result<-questionr::freq(temp.dd$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")

F7b. Have you quit smoking?
	n	%	val%
No	1	6.2	16.7
Yes	5	31.2	83.3
Scantron_Error	0	0.0	0.0
NA	10	62.5	NA
Total	16	100.0	100.0

  result<-questionr::freq(temp.dd$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")

F7BAge. If yes, At what age did you quit?
	n	%	val%
24	1	6.2	6.2
40	1	6.2	6.2
42	1	6.2	6.2
60	1	6.2	6.2
“NA”	12	75.0	75.0
Total	16	100.0	100.0

rm(temp.dd)

G1

G1. What is your current marital status?
- 1=Married, or living with a partner
- 2=Separated
- 3=Divorced
- 4=Widowed
- 5=Never Married

All data

  g1 <- as.factor(d[,"g1"])
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5",
                     Scantron_Error="*")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married","Scantron_Error"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%	%cum	val%cum
Married_partner	2239	62.9	64.4	62.9	64.4
Separated	123	3.5	3.5	66.4	68.0
Divorced	571	16.1	16.4	82.5	84.4
Widowed	209	5.9	6.0	88.3	90.4
Never_Married	332	9.3	9.6	97.7	99.9
Scantron_Error	2	0.1	0.1	97.7	100.0
NA	81	2.3	NA	100.0	NA
Total	3557	100.0	100.0	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	187	58.3	58.8
Separated	9	2.8	2.8
Divorced	66	20.6	20.8
Widowed	13	4.0	4.1
Never_Married	43	13.4	13.5
Scantron_Error	0	0.0	0.0
NA	3	0.9	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	132	62.9	63.8
Separated	5	2.4	2.4
Divorced	32	15.2	15.5
Widowed	10	4.8	4.8
Never_Married	28	13.3	13.5
Scantron_Error	0	0.0	0.0
NA	3	1.4	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	219	69.5	71.1
Separated	7	2.2	2.3
Divorced	47	14.9	15.3
Widowed	11	3.5	3.6
Never_Married	24	7.6	7.8
Scantron_Error	0	0.0	0.0
NA	7	2.2	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	175	49.2	50.4
Separated	15	4.2	4.3
Divorced	69	19.4	19.9
Widowed	23	6.5	6.6
Never_Married	65	18.3	18.7
Scantron_Error	0	0.0	0.0
NA	9	2.5	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	350	59.8	60.8
Separated	32	5.5	5.6
Divorced	110	18.8	19.1
Widowed	42	7.2	7.3
Never_Married	42	7.2	7.3
Scantron_Error	0	0.0	0.0
NA	9	1.5	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	1169	66.6	68.6
Separated	55	3.1	3.2
Divorced	241	13.7	14.1
Widowed	109	6.2	6.4
Never_Married	129	7.4	7.6
Scantron_Error	2	0.1	0.1
NA	49	2.8	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")

g1
	n	%	val%
Married_partner	7	43.8	46.7
Separated	0	0.0	0.0
Divorced	6	37.5	40.0
Widowed	1	6.2	6.7
Never_Married	1	6.2	6.7
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

G2

G2. With whom do you live? Mark all that apply.
- G2_1: 1=Live alone
- G2_2: 1=A spouse or partner
- G2_3: 1=Other family
- G2_4: 1=Other people (non-family)
- G2_5: 1=Pets

All data

  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	765	21.5	100
Scantron_Error	0	0.0	0
NA	2792	78.5	NA
Total	3557	100.0	100

  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	2309	64.9	100
Scantron_Error	0	0.0	0
NA	1248	35.1	NA
Total	3557	100.0	100

  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	487	13.7	100
Scantron_Error	0	0.0	0
NA	3070	86.3	NA
Total	3557	100.0	100

  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	78	2.2	100
Scantron_Error	0	0.0	0
NA	3479	97.8	NA
Total	3557	100.0	100

  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1",
                     Scantron_Error="*")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	199	5.6	100
Scantron_Error	0	0.0	0
NA	3358	94.4	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	83	25.9	100
Scantron_Error	0	0.0	0
NA	238	74.1	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	193	60.1	100
Scantron_Error	0	0.0	0
NA	128	39.9	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	64	19.9	100
Scantron_Error	0	0.0	0
NA	257	80.1	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	8	2.5	100
Scantron_Error	0	0.0	0
NA	313	97.5	NA
Total	321	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	22	6.9	100
Scantron_Error	0	0.0	0
NA	299	93.1	NA
Total	321	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	41	19.5	100
Scantron_Error	0	0.0	0
NA	169	80.5	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	134	63.8	100
Scantron_Error	0	0.0	0
NA	76	36.2	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	44	21	100
Scantron_Error	0	0	0
NA	166	79	NA
Total	210	100	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	9	4.3	100
Scantron_Error	0	0.0	0
NA	201	95.7	NA
Total	210	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	17	8.1	100
Scantron_Error	0	0.0	0
NA	193	91.9	NA
Total	210	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	43	13.7	100
Scantron_Error	0	0.0	0
NA	272	86.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	227	72.1	100
Scantron_Error	0	0.0	0
NA	88	27.9	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	43	13.7	100
Scantron_Error	0	0.0	0
NA	272	86.3	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	13	4.1	100
Scantron_Error	0	0.0	0
NA	302	95.9	NA
Total	315	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	27	8.6	100
Scantron_Error	0	0.0	0
NA	288	91.4	NA
Total	315	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	99	27.8	100
Scantron_Error	0	0.0	0
NA	257	72.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	195	54.8	100
Scantron_Error	0	0.0	0
NA	161	45.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	56	15.7	100
Scantron_Error	0	0.0	0
NA	300	84.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	13	3.7	100
Scantron_Error	0	0.0	0
NA	343	96.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	23	6.5	100
Scantron_Error	0	0.0	0
NA	333	93.5	NA
Total	356	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	99	27.8	100
Scantron_Error	0	0.0	0
NA	257	72.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	195	54.8	100
Scantron_Error	0	0.0	0
NA	161	45.2	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	56	15.7	100
Scantron_Error	0	0.0	0
NA	300	84.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	13	3.7	100
Scantron_Error	0	0.0	0
NA	343	96.3	NA
Total	356	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	23	6.5	100
Scantron_Error	0	0.0	0
NA	333	93.5	NA
Total	356	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	335	19.1	100
Scantron_Error	0	0.0	0
NA	1419	80.9	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	1188	67.7	100
Scantron_Error	0	0.0	0
NA	566	32.3	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	220	12.5	100
Scantron_Error	0	0.0	0
NA	1534	87.5	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	32	1.8	100
Scantron_Error	0	0.0	0
NA	1722	98.2	NA
Total	1754	100.0	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	84	4.8	100
Scantron_Error	0	0.0	0
NA	1670	95.2	NA
Total	1754	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")

g2_1: Live alone
	n	%	val%
Live_alone	7	43.8	100
Scantron_Error	0	0.0	0
NA	9	56.2	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")

g2_2: A spouse or partner
	n	%	val%
spouse_partner	8	50	100
Scantron_Error	0	0	0
NA	8	50	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")

g2_3: Other family
	n	%	val%
Other_family	1	6.2	100
Scantron_Error	0	0.0	0
NA	15	93.8	NA
Total	16	100.0	100

  result<-questionr::freq(temp.dd$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")

g2_4: Other people (non-family)
	n	%	val%
Other_non_family	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

  result<-questionr::freq(temp.dd$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")

g2_5: Pets
	n	%	val%
Pets	0	0	NaN
Scantron_Error	0	0	NaN
NA	16	100	NA
Total	16	100	100

rm(temp.dd)

G3

G3. How do you identify yourself?
- 1=Straight/heterosexual
- 2=Bisexual
- 3=Gay/homosexual/same gender loving
- 4=Other
- 99=Prefer not to answer

All data

  g3 <- as.factor(d[,"g3"])
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99",
                       Scantron_Error="*")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer","Scantron_Error"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	3257	91.6	95.2
Bisexual	22	0.6	0.6
homosexual	54	1.5	1.6
Other	22	0.6	0.6
Prefer_not_to_answer	66	1.9	1.9
Scantron_Error	1	0.0	0.0
NA	135	3.8	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	302	94.1	94.7
Bisexual	2	0.6	0.6
homosexual	8	2.5	2.5
Other	1	0.3	0.3
Prefer_not_to_answer	5	1.6	1.6
Scantron_Error	1	0.3	0.3
NA	2	0.6	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	189	90.0	91.7
Bisexual	2	1.0	1.0
homosexual	11	5.2	5.3
Other	1	0.5	0.5
Prefer_not_to_answer	3	1.4	1.5
Scantron_Error	0	0.0	0.0
NA	4	1.9	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	289	91.7	95.1
Bisexual	5	1.6	1.6
homosexual	6	1.9	2.0
Other	0	0.0	0.0
Prefer_not_to_answer	4	1.3	1.3
Scantron_Error	0	0.0	0.0
NA	11	3.5	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	327	91.9	96.5
Bisexual	1	0.3	0.3
homosexual	5	1.4	1.5
Other	0	0.0	0.0
Prefer_not_to_answer	6	1.7	1.8
Scantron_Error	0	0.0	0.0
NA	17	4.8	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	534	91.3	95.2
Bisexual	3	0.5	0.5
homosexual	5	0.9	0.9
Other	8	1.4	1.4
Prefer_not_to_answer	11	1.9	2.0
Scantron_Error	0	0.0	0.0
NA	24	4.1	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	1603	91.4	95.5
Bisexual	8	0.5	0.5
homosexual	18	1.0	1.1
Other	12	0.7	0.7
Prefer_not_to_answer	37	2.1	2.2
Scantron_Error	0	0.0	0.0
NA	76	4.3	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")

g3
	n	%	val%
heterosexual	13	81.2	86.7
Bisexual	1	6.2	6.7
homosexual	1	6.2	6.7
Other	0	0.0	0.0
Prefer_not_to_answer	0	0.0	0.0
Scantron_Error	0	0.0	0.0
NA	1	6.2	NA
Total	16	100.0	100.0

rm(temp.dd)

G4

G4. What is the HIGHEST level of education you, your father, and your mother have completed?
- 1=Grade school or less
- 2=Some high school
- 3=High school graduate or GED
- 4=Vocational school
- 5=Some college
- 6=Associate’s degree
- 7=College graduate (Bachelor’s degree)
- 8=Some graduate education
- 9=Graduate degree
- 88=Don’t know

All data

  g4a <- as.factor(d[,"g4a"])
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Scantron_Error="*")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Scantron_Error"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  g4b <- as.factor(d[,"g4b"])
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88",
                      Scantron_Error="*")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know","Scantron_Error"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  g4c <- as.factor(d[,"g4c"])
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88",
                      Scantron_Error="*")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know","Scantron_Error"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")

g4a: You
	n	%	val%
Grade_school_or_less	100	2.8	3.1
Some_high_school	259	7.3	8.1
High_school_graduate_GED	793	22.3	24.7
Vocational_school	116	3.3	3.6
Some_college	704	19.8	21.9
Associate_degree	278	7.8	8.7
College_graduate	426	12.0	13.3
Some_graduate_education	96	2.7	3.0
Graduate_degree	441	12.4	13.7
Scantron_Error	0	0.0	0.0
NA	344	9.7	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")

g4b: Your father
	n	%	val%
Grade_school_or_less	849	23.9	27.2
Some_high_school	446	12.5	14.3
High_school_graduate_GED	652	18.3	20.9
Vocational_school	88	2.5	2.8
Some_college	111	3.1	3.5
Associate_degree	51	1.4	1.6
College_graduate	103	2.9	3.3
Some_graduate_education	18	0.5	0.6
Graduate_degree	81	2.3	2.6
Dont_know	728	20.5	23.3
Scantron_Error	0	0.0	0.0
NA	430	12.1	NA
Total	3557	100.0	100.0

  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")

g4c: Your mother
	n	%	val%
Grade_school_or_less	597	16.8	19.0
Some_high_school	500	14.1	15.9
High_school_graduate_GED	893	25.1	28.4
Vocational_school	120	3.4	3.8
Some_college	164	4.6	5.2
Associate_degree	100	2.8	3.2
College_graduate	156	4.4	5.0
Some_graduate_education	22	0.6	0.7
Graduate_degree	99	2.8	3.2
Dont_know	490	13.8	15.6
Scantron_Error	0	0.0	0.0
NA	416	11.7	NA
Total	3557	100.0	100.0

rm(temp.dd)

G5

G5. Which one of the following best describes what you currently do?
- 1=Currently working full-time
- 2=Currently working part-time
- 3=Looking for work, unemployed
- 4=Retired
- 5=On disability permanently
- 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
- 7=Volunteer work/work without pay
- 8=Other

All data

  g5 <- as.factor(d[,"g5"])
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8",
                     Scantron_Error="*")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other","Scantron_Error"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	880	24.7	25.7
part_time	197	5.5	5.7
unemployed	58	1.6	1.7
Retired	1629	45.8	47.5
disability_permanently	456	12.8	13.3
disability_for_a_time	36	1.0	1.1
Volunteer_work	13	0.4	0.4
Other	69	1.9	2.0
Scantron_Error	90	2.5	2.6
NA	129	3.6	NA
Total	3557	100.0	100.0

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	89	27.7	28.3
part_time	25	7.8	7.9
unemployed	9	2.8	2.9
Retired	145	45.2	46.0
disability_permanently	30	9.3	9.5
disability_for_a_time	5	1.6	1.6
Volunteer_work	0	0.0	0.0
Other	7	2.2	2.2
Scantron_Error	5	1.6	1.6
NA	6	1.9	NA
Total	321	100.0	100.0

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	72	34.3	35.0
part_time	11	5.2	5.3
unemployed	1	0.5	0.5
Retired	91	43.3	44.2
disability_permanently	20	9.5	9.7
disability_for_a_time	2	1.0	1.0
Volunteer_work	1	0.5	0.5
Other	3	1.4	1.5
Scantron_Error	5	2.4	2.4
NA	4	1.9	NA
Total	210	100.0	100.0

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	94	29.8	30.9
part_time	18	5.7	5.9
unemployed	7	2.2	2.3
Retired	140	44.4	46.1
disability_permanently	24	7.6	7.9
disability_for_a_time	4	1.3	1.3
Volunteer_work	2	0.6	0.7
Other	11	3.5	3.6
Scantron_Error	4	1.3	1.3
NA	11	3.5	NA
Total	315	100.0	100.0

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	49	13.8	14.4
part_time	18	5.1	5.3
unemployed	8	2.2	2.3
Retired	167	46.9	49.0
disability_permanently	75	21.1	22.0
disability_for_a_time	5	1.4	1.5
Volunteer_work	2	0.6	0.6
Other	11	3.1	3.2
Scantron_Error	6	1.7	1.8
NA	15	4.2	NA
Total	356	100.0	100.0

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	130	22.2	23.0
part_time	30	5.1	5.3
unemployed	9	1.5	1.6
Retired	278	47.5	49.1
disability_permanently	86	14.7	15.2
disability_for_a_time	4	0.7	0.7
Volunteer_work	3	0.5	0.5
Other	9	1.5	1.6
Scantron_Error	17	2.9	3.0
NA	19	3.2	NA
Total	585	100.0	100.0

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	441	25.1	26.2
part_time	94	5.4	5.6
unemployed	24	1.4	1.4
Retired	802	45.7	47.7
disability_permanently	219	12.5	13.0
disability_for_a_time	15	0.9	0.9
Volunteer_work	5	0.3	0.3
Other	28	1.6	1.7
Scantron_Error	52	3.0	3.1
NA	74	4.2	NA
Total	1754	100.0	100.0

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.dd$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")

g5
	n	%	val%
full_time	5	31.2	31.2
part_time	1	6.2	6.2
unemployed	0	0.0	0.0
Retired	6	37.5	37.5
disability_permanently	2	12.5	12.5
disability_for_a_time	1	6.2	6.2
Volunteer_work	0	0.0	0.0
Other	0	0.0	0.0
Scantron_Error	1	6.2	6.2
Total	16	100.0	100.0

rm(temp.dd)

G6

G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
- G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
- G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
- G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
- G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
- G6_5: 1= Medicaid or other state provided insurance
- G6_6: 1=Medicare/government insurance
- G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
- G6_8: 1=I do not have any medical insurance

All data

  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1",
                     Scantron_Error="*")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

LA County

temp.dd <- new.d[which(new.d$siteid == "Los Angeles County.80"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Northern CA

temp.dd <- new.d[which(new.d$siteid == "Northern CA.30"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Greater CA

temp.dd <- new.d[which(new.d$siteid == "Greater CA.10"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Detroit

temp.dd <- new.d[which(new.d$siteid == "Detroit.60"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Louisiana

temp.dd <- new.d[which(new.d$siteid == "Louisiana.40"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Georgia

temp.dd <- new.d[which(new.d$siteid == "Georgia.20"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Michigan

temp.dd <- new.d[which(new.d$siteid == "Michigan.61"), ]
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_employer	1202	33.8	100
Scantron_Error	0	0.0	0
NA	2355	66.2	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")

Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
	n	%	val%
Insurance_family	371	10.4	100
Scantron_Error	0	0.0	0
NA	3186	89.6	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased directly from an insurance company (by you or another family member)")

Insurance purchased directly from an insurance company (by you or another family member)
	n	%	val%
Insurance_insurance_company	232	6.5	100
Scantron_Error	0	0.0	0
NA	3325	93.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")

Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
	n	%	val%
Insurance_exchange	105	3	100
Scantron_Error	0	0	0
NA	3452	97	NA
Total	3557	100	100

  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicaid or other state provided insurance")

Medicaid or other state provided insurance
	n	%	val%
Medicaid_state	510	14.3	100
Scantron_Error	0	0.0	0
NA	3047	85.7	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Medicare/government insurance")

Medicare/government insurance
	n	%	val%
Medicare_government	1620	45.5	100
Scantron_Error	0	0.0	0
NA	1937	54.5	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "VA/Military Facility (including those who have ever used or enrolled for VA health care)")

VA/Military Facility (including those who have ever used or enrolled for VA health care)
	n	%	val%
VA_Military	601	16.9	100
Scantron_Error	0	0.0	0
NA	2956	83.1	NA
Total	3557	100.0	100

  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "I do not have any medical insurance")

I do not have any medical insurance
	n	%	val%
Do_not_have	63	1.8	100
Scantron_Error	0	0.0	0
NA	3494	98.2	NA
Total	3557	100.0	100

rm(temp.dd)

Survey Processing All

Weijia Hu

2021-08-01 14:52:56

SITE ID

SURVEY ID

LOCATION NAME

RESPOND ID

METHODOLOGY

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A1

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A2

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A4

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A5

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A6

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A7

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

A8

All data

LA County

Northern CA

Greater CA

Detroit

Louisiana

Georgia

Michigan

B1A: Father